Te Pūnaha Matatini Whānau

Improving the tools we use to analyse citizen science data

Improving the tools we use to analyse citizen science data

Julie Mugford (pictured), a PhD student at the University of Canterbury and Chair of Te Pūnaha Matatini Whānau, is researching and developing statistical tools to improve the accuracy of classification-based crowdsourcing, aka citizen science.

Citizen science is the involvement of volunteers in helping scientists collect and analyse information, and Julie’s research aims to measure the accuracy of users and to develop efficient ways to improve the overall accuracy of such data.

Typically, classification-based citizen science projects ask multiple participants to identify each object and consensus methods are used to decide the classification of the object. Commonly, simple consensus methods – for example, majority vote – are used. However, majority vote weights the contributions from each participant equally but the participants may vary in accuracy with which they can label objects.

“Our approach is to use Bayesian statistics to estimate users’ accuracies at identifying objects and include these accuracies in the classification process,” explained Julie.

“Although this approach complicates the classification process compared to a simple majority vote rule, it improves the accuracy of the classification decisions and provides more robust measures of classification certainty.”

How Kiwis are helping to answer important scientific questions

Citizen scientists can encompass a wide range of members within our society – from school children to trained scientists – who participate in a variety of research projects. These projects are often set up and managed by professional scientists, and specifically designed to give volunteers a role. For example, sharing and classifying bird and other observations of nature, classifying land types in satellite images of Earth, or classifying galaxies.

The popularity of citizen science projects has risen enormously in the last two decades, providing researchers with access to data from a large range of locations at unprecedented frequencies with minimal costs. This has become increasingly important as costly expert resources struggle to match the effort required to answer scientific questions. However, there is ongoing debate on the usefulness and accuracy of citizen science data as it may be prone to greater variability due to differences in volunteer’s skills.

“Motivated by the vision of Biosecurity New Zealand to have a biosecurity team of 4.7 million [New Zealand’s resident population], we have initially focused on improving the accuracy of classification-based citizen science projects that could be used as a tool to monitor invasive pests in New Zealand,” said Julie.

Biosecurity New Zealand, a part of the Ministry for Primary Industries, has set out a vision for 2025, and one of its five strategic directions aims is to make all New Zealanders aware of the importance of biosecurity and to get them involved in pest and disease management. It hopes to encourage a collective effort across the country – in which ‘every New Zealander becomes a biosecurity risk manager and every business manages their own biosecurity risk’.

 

Inclusive learning environments for both Māori and Pākehā

Inclusive learning environments for both Māori and Pākehā

Improving education for all is as uncontentious as political clauses get here in Aotearoa, championing this issue is on the front burner of nearly every government body, institution, and organisation across the country. Compare this to the endless debate, controversy, resistance, and even hostility which meets attempts to address protocol and resolve for our Māori students.

He waka eke noa and ‘Auckland 2050’

This year’s research project at Te Pūnaha Matatini, ‘He waka eke noa’, is part of an ongoing research partnership with Ngāti Whātua Ōrākei that aims to highlight some features of our education system in Aotearoa—chiefly those affecting Māori—as part of Auckland Council’s greater 30-year vision, ‘Auckland 2050’.

He waka eke noa follows in the wake of last year’s research, ‘Analysis of Well-Being’, which endeavoured to interpret open responses of 684 registered hapū members, taken from surveys encompassing various forms of codified knowledge and kōrero in the hopes of directing the development of a hapū well-being framework. Well-being was measured with regards to life satisfaction, education, housing, proficiency and abilities in te reo and, more generally, te Ao Māori. Last year’s well-being research proposal was awarded Marsden funding through the Ministry of Business, Innovation and Employment’s (MBIE) Te Pūnaha Hihiko: Vision Mātauranga Capability Fund, which invests in ‘the development of skilled people and organisations undertaking, research that supports the themes and outcomes of our Vision Mātauranga policy.’

A major component of our research cannot be revealed here for privacy reasons, but for the purpose of sharing some of the motivations and ways we engaged with our research, we can look at the educational climate of Aotearoa as a whole, indigenous ways of knowing, and the shared stake we have as a community to improve mainstream education for our Māori students.

Using data collected by the Ministry of Education, we constructed the below graphs to show 2017 post-secondary completions by subject. We found that Māori students were either largely underrepresented or overrepresented across major subjects. Further, we saw more spread in subjects taken amongst other ethnicities while Māori students predominantly stayed in familiar domains—especially at earlier levels of study.

However, there seems to be more spread in subjects once Māori students reach a Bachelors level. This may suggest continuation in studies can affect the way in which Māori students engage with subjects- branching out into different fields after acquiring confidence in mainstream education institutions.

Research in keeping with te Ao Māori perspectives 

Owing to our shared identities of being both Māori Pākehā, as well as students- we attribute a lot of strength throughout this project in our ability to appreciate the unique issues and challenges faced by students with shared membership. We also acknowledge the capabilities for dynamism and resilience. From the outset, a major goal was to utilise our respective disciplines for research while keeping the essence of te Ao Māori perspectives alive throughout. Our team engaged in quantitative analysis as well as discourse analysis pulling from a multitude of various texts that laminated on indigenous taxonomy and concepts surrounding Mātauranga Māori.

Mātauranga Māori embodies a complex network of codified systems of knowledge transfer and storage to which universal constructs are framed in both past and present, existing and non-existing. Cognisance and assessment of information and protocol envelops moteatea (chants, poems), whaikorero (oratory, speechmaking), maramataka (calendar), waiata (songs), pepeha (quotations), whakataukī/whakatauki (proverbs), whakapapa (genealogies) and pūrākau (stories)—each with its own categories, style, complex patterns and characteristics’ (Lee J, 2008).

This culturally-embedded system founded on kanohi kitea (face-to-face) interactions between individuals, whānau, hapū, and iwi is an all-encompassing body of knowledge based on evidence, culture, values and worldview. Despite being a rich form of gathering and sharing information, these traditional ways of knowing have often been considered incompatible with local pools of thought and sanctioned as illegitimate in the wider scientific and academic communities.

In rumination of Aotearoa’s current educational climate, it’s hard to imagine that the above mentioned would not play a significant role in how Māori students interact with mainstream constructs of knowledge as well as the capacities of education providers, peers, and the public to appropriately assess and acknowledge learning systems so undivulged in the domains to which they usually operate. We might also consider how the role of whānau can be affected in the enormous task of accessing and providing tools relevant to outside industries of learning and their abilities to advocate on behalf of their tamariki far from the sources of support provided within their everyday communities. As Māori student education figures indicate improvements in academic participation, the alarmingly high SSEE rates (stand-downs, suspensions, exclusions and expulsions)— at nearly all levels of study— only corroborates the narrative that the current education system is failing. The system in place is robbing its students of having an equal chance to prosper in falling short of its most essential obligations– to encourage and inspire student potential. The damages placed on already strained communities to which these students are part of seems to fuel a never-ending cycle that sets up generation after generation with even less opportunities to thrive.

Unfortunately, the displacement of Māori is not consigned to the past. Historical attempts to keep te Ao Māori outside of the local mainstream includes legislation such as the Tohunga Suppression Act, the Education Ordinance Act and the Native Schools Act, and a nationwide ban on te Reo Māori, including ‘A wide range of punishments used against children who speak te Reo at school (including corporal punishment)’.

Amid cultural asphyxiation, Māori risk losing their voice, abilities to navigate on their own terms, and essentially – their mana. Even in moments of advocacy we are limited in our capacities. We look at issues surrounding Māori and the needs for address but from a Māori standpoint, there is a need for redress. An overwhelming loss of trust in the mainstream educational institutions prevails when we miss these opportunities of knowing.

Conclusion

In conclusion, institutions cannot remain mutually exclusive in inclusive learning environments. There is a demand for institutes for Māori and Pākehā across Aotearoa, to foster a respectful, understanding and empathetic community. Culture is a learned system. Access to Māori systems of support in schools will elevate New Zealand children’s comprehension of how culture, language, and heritage empower their own identities. The immersion of these two systems of education will lead to a broader sentiment of cultural heritage will encourage New Zealand’s future communities to practise more tolerance and acceptance of cultural diversity.

He waka eke noa in a literal sense translates to the ‘canoe which we are all in without exception’. For the purposes of this project, we might attribute this whakataukī to a collective consciousness and gentle reminder that, when we are in a waka, there is unity in a shared purpose. Here, we look to education.


Authors

Brianne (Bri) Halbert and Megan Liejh are students at the University of Auckland. Bri is pursuing a double major in Computer and Data Science, and Megan is completing a conjoint Law (Hons) and Arts Degree in Political Philosophy Law and Politics. While these disciplines may appear vastly different, they were able to find a lot of overlap and even harmony in their exploration of inclusive education for Māori.


Reference

Lee J. 2008, Ako: Pūrākau of Māori teachers’ work in secondary schools. [Unpublished PhD thesis]. Auckland, New Zealand: University of Auckland.

Designing a data management system for archaeological records

Designing a data management system for archaeological records

The Archaeology and Physics Departments at the University of Auckland, as well as contributors from other universities, have been collecting data on obsidian artefacts from the north part of New Zealand. To date, this project has data on over 2,500 such artefacts, obtained from various sources including historical studies done on obsidian to more recent studies done by current archaeologists at the University of Auckland. Part of the aim of this research is to look at “Social Network Analysis of Obsidian Artefacts and Māori Interaction in Northern Aotearoa New Zealand” which is the title of a recent publication which involved my Te Pūnaha Matatini and industry supervisors.

Why study obsidian?

Obsidian is a volcanic glass which is found at several locations in New Zealand. It is hard and brittle such that when a piece is broken off (called a flake), it has sharp edges. This made it very useful as a cutting tool in pre-European New Zealand. By analysing the elemental compounds of the artefacts, it can be determined where each artefact was sourced. By comparing this to which archaeological site each artefact was found at, my supervisor Dr Dion O’Neale has been able to infer social networks of pre-European New Zealand. Dion analysed the geographical least cost paths and found that distance was not always the main factor in determining where each archaeological site sourced its obsidian flakes from. Therefore, by analysing obsidian artefacts, a lot of information can be gained and it is the aim of this research project to be able to infer this type of information and even more regarding pre-European Aotearoa New Zealand.

With so much varied data the need arose to have a central data infrastructure where all the various data records can be stored along with protocols to support data quality and provenance. This data needed to be accessible by various parties from various departments and universities.

The main steps I took to complete my internship project included:

  • Choosing and learning to use an appropriate database software
  • Schema design
  • Data cleaning
  • Scripting for automated data uploading

These steps were not necessarily sequential and often ran in conjunction with each other. For example, since there was a variety of data sources, while I was doing the data cleaning I came across new data fields in which case I had to edit the schema to reflect the new field. However while doing the data cleaning, I often came across discrepancies or unknown variables in the data which I needed to wait to hear back from other people about before I could proceed.

It surprised me how long it took me to design the schema. Data cleaning often takes the longest amount of time. In some sense the data cleaning did take some time because while I was designing the schema, I was also figuring out what data to keep and what not to keep. This greatly reduced the time it took for me to clean and format all the data tables to be ready for upload. After that, finding and learning to use an appropriate database platform also took a while. Finally, writing the scripts for automatic uploading to the database took a couple of weeks.


Author

Kate is currently studying for a Master of Applied Data Science (MADS) at the University of Canterbury. 

Machine learning for te reo Māori

Machine learning for te reo Māori

For 10 weeks over the 2018-19 summer, I was involved in a project with Te Hiku Media and Dragonfly Data Science to aid in the development of a Māori voice assistant. The motivation for this project was to make Te Reo Māori more accessible and fun in the digital age.

During my internship I achieved the creation of a “box” called Rapere (translation of “Raspberry” into Te Reo Māori) containing a Raspberry Pi computer which is connected to the internet, some lights, a speaker and a microphone. This box has been coded to be continuously listening for spoken voice, and when this is detected it records what is being said until there is a longer break in the speaking (this file is overwritten each time a recording is made).

Cherie’s recording equipment set-up.

The recording is transcribed using Te Hiku Media’s Application Program Interface on koreromaori.io. The transcription that is returned to the box is compared to some key words which mean the speaker is likely asking to hear the news or to listen to the radio, or to stop playing. If these are heard, then the news or radio stream is played or stopped, and otherwise it goes back to listening for these phrases. The phrase “kia ora” lights up an LED for a few seconds. The box is able to listen for commands while playing audio, which allows the user to stop audio playing. The project was documented and all the code uploaded online to allow other developers at Te Hiku Media to progress it further and demonstrate the abilities of the Rapere box.

I experienced a great feeling of accomplishment from my work with Te Hiku Media and Dragonfly Data Science. Going from a bunch of components and an empty raspberry pi computer to having a working program with two different APIs and which plays the news on my correctly saying the appropriate phrase in Te Reo was more than I thought I would be able to achieve and I am proud of what I achieved with the help of my supervisors. I am grateful to Te Pūnaha Matatini for providing me with the opportunity to have this internship.


Author

Cherie Vasta is a student at the University of Canterbury who is going into her final year of a Bachelor of Engineering in Mechatronics. Cherie enjoys problem solving and working as a part of a team. She is excited by working with high-end technologies, as she would like to be at the forefront of engineering the future.

Streamlining the process of social network analysis

Streamlining the process of social network analysis

Romalee Amolic talks about her 2018-19 Te Pūnaha Matatini Summer Internship with AgResearch where she worked on a project to enhance social network analyses of biosecurity information in the New Zealand tourism industry, so that such analyses can be conducted faster and more effectively in the future.

Social Network Analysis (SNA) is a powerful data analysis technique which often helps in identifying hidden relationships and other critical information in a communication network.  Data for an SNA can be collected from various sources which may result in extensive pre-processing and cleaning time as compared to the time needed for actual network analysis. Hence, this project aimed to use data carpentry to streamline the use of social research data (e.g. collected through surveys) to be able to conduct social network analyses quicker and more effectively in the future.

Better Border Biosecurity case study

The Better Border Biosecurity project, a multi-partner, cooperative science collaboration which analyses the exchange of biosecurity information in the New Zealand tourism industry, is used as a case study to develop and test methods which streamline the SNA process. The data for this project includes 154 responses from tourism providers across New Zealand who named up to 3 sources from whom they seek or receive biosecurity information. Information about the location and role of the respondents was collected. Some additional questions were also included in the survey such as the form and frequency of communication, the usefulness of information and the trust between parties.

This information was then used to perform Social Network Analysis in Gephi – a powerful interactive social network analysis tool. However, the survey data had to be first converted into a format fit for network analysis. The conventional approach for cleaning the data is discussed below.

Conventional approach

The survey responses were collected in Excel sheets. The data pre-processing and cleaning was done manually using Excel.

Problems with the conventional approach

  • It involved dealing with the data manually which was an extremely time-consuming process and needed about 1-3 weeks depending on the complexity of the data.
  • The process was prone to human errors which reduced the potential of the data.
  • It required skilled labour for an extended amount of time and hence, increased the costs involved.
  • It led to data inconsistencies.

Hence, taking all these problems into consideration, a generic automated process was developed to clean the data as discussed in the following sections.

Data Cleaning and Pre-processing

In a network, each node represents a unique identity. Hence, the most important task in cleaning the data was to recognise and remove inconsistencies in the names which occurred due to the textual nature of responses. The following techniques were used to clean the data in Python:

  • Initially, all the names were made lowercase for the analysis.
  • Special characters were removed.
  • Rows containing missing or no information were removed.
  • Trailing or unnecessary white spaces were removed.
  • Incorrect spellings were identified and removed using a spellchecker. The challenge here was to differentiate between the proper nouns (such as ‘EcoZip’) and dictionary words (such as ‘Adventures’). For example, an entity name ‘EcoZip Adventures’ was misspelled as ‘EcoZip Adventres’. A conventional spell checker would consider ‘EcoZip’ as a spelling error along with ‘Adventres’ as both the words are not found in the dictionary. Hence, a solution was developed to distinguish the proper nouns from actual dictionary words in entity names and correct spelling errors in the data.

  • A custom algorithm was developed to identify abbreviations in the text and replace it with the full name. e.g. ‘DOC’ was identified as ‘Department of Conservation’.
  • Several names which were written similarly but were however, the same entity, were identified and merged. This is the most significant part of the process or the most “satisfying” part, as a user described it. An example is shown below:

  • All the names in the network were also compared pairwise to further remove any inconsistencies and generate a list of consistent and unique names involved in the biosecurity information exchange.

This cleaning process reduced the entities from 319 (including inconsistencies) to 139 consistent and unique entities (nodes) in the network with 335 relationships (edges) between them which were then used to generate visualisations.

Social Network Analysis

Directed maps were generated using Gephi which were then further analysed. An anonymised example of one of the social network maps generated is shown below:

An example of anonymised social network map generated using Gephi.

The information obtained through this network analysis can now be used by biosecurity providers to better target information exchange within the New Zealand tourism industry.

Achievements

  1. This application significantly reduced the time (from 1-3 weeks to a few hours or minutes) in cleaning and pre-processing the data before analysis.
  2. As a result, the costs involved in the conventional extensive processes, which involved a lot of manual effort, were also reduced.
  3. The new streamlined process almost eliminated the human errors involved in the manual inspection of data.

Conclusion

Hence, through this case study, an application was developed, which streamlines and automates all the steps starting from loading and cleaning the data up to the generation of data sheets to be used in the SNA. Although, this is currently a Python application, the development of a GUI based interactive SNA application design is currently under consideration.

I would like to acknowledge Helen Percy, my industry supervisor and Penny Payne, the social scientist at AgResearch for their invaluable support during this project.


Author

Romalee Amolic is a Master of Applied Data Science student at the University of Canterbury. In February 2018, she completed her summer internship with AgResearch, Hamilton. She thoroughly enjoyed her internship project which involved streamlining and increasing the efficiency of the data cleaning and network map generation processes at AgResearch. She is passionate about harnessing the power of data analytics to improve the lives of people. She eagerly looks forward to applying the skills learnt, in fulfilling her aspiration of becoming a data scientist.

 

Romalee (centre) with Helen Percy, her industry supervisor (right), and Penny Payne, the social scientist at AgResearch (left).

Viel Glück Dr Demi!

Viel Glück Dr Demi!

Congratulations to Demival Vasques Filho (Demi), our latest student to successfully defend his PhD thesis.

Demi undertook his PhD on ‘Structure and dynamics of social bipartite and projected networks’ at the University of Auckland, under the supervision of Te Pūnaha Matatini Principal Investigator Dion O’Neale. He now leaves us to take up a new position at the Leibniz Institute of European History in Mainz, Germany.

Thank you Demi for being such an active part of TPM over the years. We will miss you! Kia kaha!

 

 

Demi (left) with his PhD supervisor Dion O’Neale (second from right) and two of his examiners, Uli Zuelicke (second from left) and Scott Parkins (right).

Bon voyage Kyle Higham, our latest PhD graduate

Bon voyage Kyle Higham, our latest PhD graduate

Congratulations to Te Pūnaha Matatini PhD student Kyle Higham, our much admired and highly active TPM Whānau past-chair and member, who successfully defended his PhD thesis recently.

Kyle undertook his PhD at the Victoria University of Wellington, researching knowledge diffusion and the dynamics of citation networks under the supervision of TPM investigators Adam Jaffe, Michele Governale and Uli Zuelicke.

Always a popular figure at TPM gatherings, Kyle now leaves us to take up an exciting role at the prestigious Ecole polytechnique fédérale de Lausanne (EPFL) in Switzerland.

Well done for all that you’ve achieved Kyle, and thank you for all your work with TPM Whānau. We hope to see you back in the future. Ka kite anō, kia kaha!

Kyle (left) and the TPM Whānau at one of their recent retreats.

Te Pūnaha Matatini farewells Samin Aref

Te Pūnaha Matatini farewells Samin Aref

Congratulations to Samin Aref, a highly valued member of the TPM Whānau, for handing in his PhD thesis recently. Samin commenced his PhD under the supervision of TPM Investigator Mark Wilson, working on computationally intensive problems in complex networks. Samin has the honour of being the first ever TPM intern to graduate with a PhD.

Another reason to celebrate is that Samin has secured a post-doctorate position at the prestigious Max Plank Institute for Demographic Research in Rostock, Germany.

Samin flew out to Europe earlier this week. However, not before we were able to give him a proper send-off – during the TPM Whānau Retreat in Ōtaki and subsequently at TPM HQ in Auckland. Samin’s supervisor Mark Wilson and our director Shaun Hendy gave speeches recognising his contributions during his time with TPM.

Kia kaha, all the best in Germany Samin! Stay in touch and we hope to see you back in Aotearoa in the future!

Māori and Pacific Island women in science

Māori and Pacific Island women in science

Before I started working as a research assistant on the Hidden Networks project, the only woman from the history of New Zealand science I could name was Joan Wiffen, the “dinosaur lady” who discovered New Zealand’s first dinosaur fossils in Hawke’s Bay. She was a remarkable woman who contributed much to palaeontology here in New Zealand; she was also, incidentally, very white. I too am outwardly (that is, I pass as) very white. But as a mixed-race woman of Samoan descent, when I started this project I was very interested to learn about the contributions of non-Pākehā – chiefly, Māori and Pacific Island – women to science in Aotearoa. For the purposes of my research, I’ve taken “woman in science” to broadly mean a woman who has made a contribution to science in New Zealand, including both professional scientists with academic backgrounds and amateur scientists who have added to the pool of knowledge in their field, like Joan Wiffen.

The more I researched, the whiter the history of women in science in New Zealand came to look. Unsurprising really: according to Elizabeth McKinley, in 1998 just 1.5% of total employees at seven Crown Research Institutes in New Zealand identified as Māori women; there were none in management positions, and only two scientists. In ‘Finding Matilda’, Kate Hannah notes that “the historiography of science in New Zealand … tends to inadvertently reinforce [the] camouflage” of women. They are marginalized, but not absent: if you go looking, as I have, you’ll find a staggering number of women in New Zealand science from the 14th century to present-day. Yet from the beginnings of European presence in New Zealand, the overwhelming majority of these women were white. A feminist revisionist history of science aims not only to make science less male-centric (i.e. demonstrate, through promotion of women’s work both quantitatively and qualitatively, that science never has been just a man’s world) but also to make it less monochromatic (so to speak), which means celebrating the scientific achievements of brown women in New Zealand’s history, and showing that science never has been just a white world either.

In fact, the first women who made scientific contributions in Aotearoa were not Pākehā but Māori. I was delighted to learn of Whakaotirangi, who in the 1300s “was responsible for safeguarding the seed of the kūmara” as the Tainui Waka journeyed to Waikato. She was the wife of Hoturoa, the leader of the Tainui Waka migration from Hawaiki to Aotearoa, but also an important historical figure in her own right. In ‘Whakaotirangi: A Canoe Tradition’, Diane Gordon-Burns and Rāwiri Taonui explore how her importance has been diminished in post-European contact accounts of the Tainui migration. Tainui and Te Arawa traditions both speak of Whakaotirangi: she appears to be a noble and important ancestor in the history of both iwi. While she is most remembered for bringing kūmara to Waikato, she was also responsible for a number of other plants brought from Hawaiki. On arrival in Waikato, Whakaotirangi built gardens in which she experimented with growing and tending to a variety of plants, both for sustenance and medicinal purposes. She discovered how to make the kūmara, which had come from a much warmer climate, grow in the cooler land her people had settled. Her work was crucial for the establishment of the Tainui people: it provided them with a reliable food supply as they adjusted to life in a new land. She was also involved in commissioning, building and launching the Tainui canoe. Her profile on the Royal Society of New Zealand website, as part of their series 150 Women in 150 Words, credits her as “one of New Zealand’s first scientists”.

Around the middle of the 1400s, another important ancestor of the Waikato people appeared. Kahu (also known as Kahupeka, Kahupekapeka, Kahukeke, or Kahurere) was a Tainui woman who experimented with plants – such as harakeke, koromiko, kawakawa and rangiora – as medicinal remedies. She did so during her great journey: walking inland through the King Country while grieving the death of her husband (who in some accounts is Rakataura; in others Uenga). She gave names to different sites along her journey (such as Te Manga-Wāero-o-Te Aroaro-ō-Kahu – ‘the stream in which Kahu’s dogskin cloak was washed’) – these names tell the story of her journey and preserve the history of the land. At some point during her journey she was ill, which may have been why she sought out plants for their medicinal properties. Unfortunately there are many different versions of Kahupeka’s story, and in them there are few mentions of her medicinal experimentations with indigenous flora. In some versions Rakataura doesn’t die, and he and Kahu traverse the countryside naming places together, as explorers.

In Māori culture, practitioners or experts in any skill or art are known as tohunga. The Tohunga Suppression Act 1907 made tohunga status a punishable offence. The Act was repealed only in 1962, and so much of the knowledge surrounding this customary way of knowing has been suppressed – my search for tohunga wahine (female practitioners) who might count as women of science has not produced significant results. However, it is worth noting that the sources I accessed relied upon the written record. Other sources, such as Māori oral histories, may be much more fruitful.

The next Māori woman in science that I was able to find wasn’t born until the 19th century. Makereti Papakura (Margaret Pattison Thom; she also went by Maggie and was of Te Arawa and Tuhourangi iwi) was born to a Māori mother and an English father in the Bay of Plenty in 1873. She was raised by her mother’s aunt and uncle in Parekarangi, a rural area. She didn’t learn English until she was ten years old, speaking only Māori until her father took over her education. After her schooling, Papakura moved to Whakarewarewa, where she became an accomplished tourist guide. She gave herself the surname Papakura after a nearby geyser when a tourist she was guiding asked if she had a Māori surname. Clearly, the name stuck. In 1891 she married surveyor Francis Joseph Dennan; they had one child together before divorcing in 1900. In 1905 she wrote Guide to the hot lakes district. Papakura travelled to England in 1912, and married Richard Charles Staples-Browne. She had first met Staples-Brown when he was on a tour of New Zealand, and had reconnected with him while she was part of a Māori tour party in England. They divorced in 1924, but Papakura remained in England and in 1926 she enrolled at Oxford University, studying a BSc in anthropology. She died on April 16, 1930, only two weeks before her thesis, The old-time Māori – in which Papakura combined customary knowledge with scholarly conventions – was due to be examined. It was published posthumously, eight years later. Her thesis covers Māori social and familial structures, housing, weaponry and relationship with fire. She was meticulous in her writing, and wrote letters to her people in New Zealand during her drafting process, to ensure her account was as accurate as possible.

Bessie Te Wenerau Grace (1889-1944; Ngāti Tūwharetoa) was the first Māori woman university graduate, graduating from Canterbury University with a BA in 1926. She was the granddaughter of Ngāti Tūwharetoa chief Horonuku Te Heuheu. She then went on to receive an MA with first-class honours in modern languages from London University. In London she also became a nun, Sister Eudora. She worked as headmistress of St Michael’s School in Melbourne. In 1945, Dame Mira Szászy (1921-2001; Ngāti Kurī, Te Rarawa, and Te Aupōuri), a prominent Māori leader, became the first Māori woman to graduate with a degree from the University of Auckland. She went on to complete a postgraduate diploma in social sciences from the University of Hawaii and worked hard to improve the welfare of Māori women throughout her life. In 1949, Rina Winifred Moore (1923-1975; Ngati Kahungunu, Rangitane and Te Whanau-a-Apanui) graduated from the University of Otago with a Bachelor of Medicine and Bachelor of Surgery – and in so doing, became the first Māori woman doctor in New Zealand. In her career she worked to improve public perceptions of the mentally ill and was one of the first doctors in New Zealand to prescribe the contraceptive pill.

It has been harder for Māori and Pacific Islanders to enter scientific professions, as they are forced to combat social prejudices that expect them to fail – that tell them this is not where they belong. It has been harder for women to enter scientific professions because, again, they have to fight against the social biases that tell them ‘this is not your world’. Until the late 20th century, many women were expected to give up their careers when they married – motherhood and the domestic sphere became their full-time responsibilities. Some women chose to remain unmarried and childless in pursuit of scientific careers, while others stopped working when they married. Māori and Pacific women have to fight both gender and racial biases for their place in the world of science. This has been the case throughout the post-contact history of Aotearoa, and continues to be so.

Dr Ocean Mercier. Image courtesy of Dr Mercier and Image Services, Victoria University of Wellington.

Today, there are increasing numbers of Māori and Pacific Island women in science, with some of them working at the intersection of traditional knowledge and western science. Dr Ocean Mercier (Ngāti Porou) is a Senior Lecturer in Māori Science (the intersection of western science and mātauranga Māori) at Victoria University of Wellington. She has a PhD in Physics and was awarded the New Zealand Association of Scientists (NZAS) inaugural Lucy Cranwell Medal (previously the Science Communicators’ Medal) in 2017. Science researcher Hokimate Harwood (Ngāpuhi) combines western scientific and Māori customary knowledge in her research of the feathers in kahu huruhuru (feather cloaks). Her use of microscopy to identify the origins of feathers used in precious cloaks has been pioneering. She is a Bicultural Science Researcher at Te Papa. Her sister, Dr Matire Harwood (Ngāpuhi; PhD MBChB), is a Senior Lecturer at the University of Auckland Medical School and has done crucial research into indigenous healthcare throughout her career. Her efforts have been widely recognised, and in 2017 she was awarded a fellowship to the L’Oréal UNESCO For Women in Science programme.

Dr Hiria McRae. Image courtesy of Dr McRae and Image Services, Victoria University of Wellington.

Victoria University science educator Dr Hiria McRae (Te Arawa, Tūhoe, Ngāti Kahungunu) has created and developed a new educational model aimed at raising Māori students’ engagement in high schools. Through her research projects she has made important contributions to the field of Māori education.

Dr Pauline Harris. Image courtesy of Dr Harris and Image Services, Victoria University of Wellington.

Victoria University astrophysicist, science lecturer and research fellow Dr Pauline Harris (Rongomaiwahine and Ngāti Kahungunu), who has a PhD in astroparticle physics, is a key figure in the revitalisation and teaching of Māori astronomy. She is also involved in the search for extra-solar planets. Connected to Harris’s Māori astronomy programme is Pounamu Tipiwai Chambers, an undergraduate student at Victoria University who has employed Māori astronomical and navigational knowledge in undertaking waka voyages across the Pacific.

Another remarkable young woman, Alexia Hilbertidou (of Greek and Samoan descent), has founded GirlBoss New Zealand, an organisation aimed at the empowerment of young women in STEM studies after she felt alienated as the only girl in her year thirteen physics for engineering class. She was also part of NASA’s SOFIA project, making her the youngest person ever to be part of a NASA mission.

My blog post aims to contribute towards the unmasking of Māori and Pacific women’s contributions to science in both historical and contemporary landscapes. We are already seeing some important changes: many Māori women in science today combine customary and scientific knowledge to great success, a road paved by Makereti Papakura and her BSc thesis. However, Māori and Pacific women are still dramatically under-represented in fields of science, particularly at senior and management levels. It is therefore important that we keep up the momentum of positive change not only by looking forward but also by looking back: the successes of past figures provide an encouraging bevy of ‘shoulders to stand on’ for women in science today.

This post was written as part of my summer scholarship research on the Hidden Networks project, supervised by Rebecca Priestley and Kate Hannah.

Further reading

If you’re interested in learning more about the women I’ve mentioned, you might enjoy some of these sources:


Author

Beth Rust is a BA(Hons) history graduate from Victoria University of Wellington. For her Honours thesis she researched the writings of Christine de Pizan, a 15th-century humanist and early defender of womankind. This past three months she has been working as a research assistant on the project ‘Hidden Networks: hybrid approaches for the history of science’. Beth is just about to start a job in the public service, and she is very excited to take the skills she has learned from her summer research into her new role. She loved being a summer scholar.

How machine learning can perpetuate racism

How machine learning can perpetuate racism

I wrote this algorithm to classify people by gender, but one of the biggest things I learned was how machine learning can reinforce racism and perform poorly on ethnic minorities.

Machine learning – or programs that are able to learn from and improve on past experience and data – is often accused of reinforcing human biases such as racism and sexism. However, it can be a bit unclear how exactly this happens.

How does an automatic soap dispenser fail to recognize black people’s hands? How does image recognition software come to classify people in kitchens as women, regardless of their actual gender? How does artificial intelligence that seeks to predict criminal recidivism produce results that are consistently biased against black people?

This walk-through hopes to give you a bit of an insight into one example of racism in machine learning, and how this comes to be.

The algorithm will be used as part of research into gender equity in STEM fields in New Zealand. A lot of information about who works in certain research centres or who graduated from university is publicly available online (for example, here are university records from NZ between 1870 and 1961), but it doesn’t explicitly include their gender. While a person reading the information can usually guess their gender quite easily and with a high degree of accuracy, it’s obviously very impractical to read and classify thousands or hundreds of thousands of observations. This is where this algorithm hopes to simplify and speed up the process of identifying women in STEM fields.

Training and testing data: Selecting appropriate data

Getting good data for the training and test sets is a really important part of machine learning. Your model is only as good as the data you train and test it on, so getting this right is key.

The starting point of my dataset is the 100 most common names for boys and girls born in New Zealand in each year, going back to 1954. One major drawback of this dataset is that it only includes people born in New Zealand, not those that emigrated here. This means the dataset is almost exclusively made up of Anglo-Saxon names, and does not reflect New Zealand’s large Asian and Pacific populations.

It also doesn’t include any Māori names, presumably because the Māori population isn’t large enough for these names to make the top 100 list. I’ve tried to remedy this by adding the top 20 Māori names for boys and girls from several years to the dataset. However, 91% of the training dataset is still made up of Anglo-Saxon names, while only 9% is made up of Māori names.

These biases in the training dataset mean that the model is likely to recognize the patterns that indicate gender in Anglo-Saxon names, while not picking up on patterns that indicate gender in the names of other cultures. The same biases in the testing dataset mean that the accuracy of the model probably only applies to Anglo-Saxon names, and that it may do much worse on names of other nationalities.  

Selecting useful features for the algorithm

It’s important to consider what features would be most useful in predicting the desired classes. I started off by using the last letter of each name to predict gender. Most Anglo-Saxon names for men end with a consonant, while most Anglo-Saxon names for women end with a vowel.

There are also some pairs of letters that are more common for one gender than the other. For example, the last letter ‘n’ is indicative of a male name (e.g. Brian, Aidan, John), but the suffix ‘yn’ is indicative of a female name (eg. Robyn, Jasmyn). Because of this, using both the last letter of each name and the suffix as features results in higher accuracy than just using the final letter. This gave me an accuracy of about 73% on a testing dataset that includes both Anglo-Saxon and Māori names.

This overall accuracy is lower than it would have been on a testing dataset made up of only Anglo-Saxon names because these features don’t perform as well with names of other origins. In a New Zealand context, this causes the most problems with Māori names. Most Māori names end in vowels, regardless of gender (examples of male Māori names include Tane and Nikau, while female Māori names include Aroha and Kaia). This means this particular feature doesn’t do a very good job with names of Māori origin.

The same problem would likely apply to other ethnicities, too. For example, Japanese, Chinese, Vietnamese, Italian and Hispanic names all often end in vowels, regardless of gender.

Imbalanced classes and the problems they cause

Imbalanced classes, or classes that are very different in their size, can also create problems for machine learning algorithms. In this case, ethnicity is an imbalanced class that is likely to influence people’s names. In the 2013 census, 74% of New Zealanders identified as European, 15% as Māori, 12% as Asian and 7% as Pacific. (Note that Statistics New Zealand allows you to identify with more than one ethnicity, therefore these numbers don’t add up to 100%).

Imbalanced classes often result in high accuracy within the majority class (in this case, European) and low accuracy within the minority classes (Māori, Asian and Pacific). This particular algorithm has an overall accuracy of about 73%. The accuracy within Māori names is about 69%, while the accuracy within European names is 75%.

The class imbalances in the data explain why the overall accuracy may not be a very good way of assessing whether the algorithm is working well. As well as checking the accuracy within each subgroup, it can be a good idea to look at precision and recall for more information on where the algorithm is doing well and where it’s doing poorly.

Precision tells us how much of a classified group actually belongs to that group. In this case, for example, precision of female names is the percentage of names classified as female that are actually female. It is calculated by dividing the number of true positive (number of women classified as female) by all positives (number of women and men classified as female).

Recall is the percentage of a particular group that has been classified as belonging to that group. For example, recall of male names is the percentage of male names that have been classified as male. Recall is calculated by dividing the number of true positives (number of men classified as male) by the number of true positives and false negatives (number of men classified as female).


The tables below show the precision, recall and a couple of other metrics on how well the algorithm is doing. The differences between the overall table and the tables by ethnicity show that it’s likely that this algorithm is systematically worse with non Anglo-Saxon names, specifically Māori names in this instance.

Overall:

precision recall F1 score support
F 0.77 0.76 0.77 274
M 0.71 0.72 0.72 226
avg/total 0.74 0.74 0.4 500

For Māori names only:

precision recall F1 score support
F 0.75 0.88 0.81 17
M 0.33 0.17 0.22 6
avg/total 0.64 0.70 0.66 23

Here we can see that both precision and recall is very low for male Māori names. This means that only a small percentage of the names classified as being male actually are male (low precision) and an even smaller percentage of male Māori names have been classified as being male (low recall).

This is probably because most Māori names end in vowels, regardless of their gender. The algorithm does alright on female Māori names, because it has seen many instances of female names ending in vowels before. But it hasn’t seen many male names ending in vowels, so it fails to classify most of these names correctly.

For European names only:

precision recall F1 score support
F 0.82 0.72 0.77 140
M 0.7 0.81 0.75 115
avg/total 0.77 0.77 0.77 255

Because machine learning algorithms with imbalanced classes usually do worse in the smaller classes, they can further marginalise minority groups by routinely misclassifying them or failing to take into account patterns that are unique to the smaller group. In this example, this is likely to be the case with ethnic minorities.

It seems that this algorithm is likely to really only do a good job on Anglo-Saxon names. This limits the situations in which it would be appropriate to use it, and risks reinforcing Eurocentricity and a focus on whiteness.

This example shows how difficulties in getting hold of representative datasets, selecting features and unbalanced classes can cause algorithms to perform poorly on minority groups. These are only a couple of the many ways machine learning can contribute to the marginalisation of minorities, and it’s important to consider how this might happen in the particular algorithm you’re working on.

The consequences of bias in machine learning can range from the irritation of not being able to get soap out of an automatic dispenser, to the devastation of being given a longer prison sentence. As these algorithms become more and more ubiquitous, it is essential that we consider these consequences in the design and application of machine learning.

See this paper for a more detailed look at how imbalanced classes affect machine learning algorithms.


Author

Emma Vitz is a recent Statistics & Psychology graduate of Victoria University who is starting a new role at an actuarial consulting company in Auckland. Emma enjoys applying data science techniques to all kinds of problems, especially those involving people and the way they think.