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Record W6906732736 · doi:10.17863/cam.118737

The Politics of Numbers: Statistical Fairness, Market Justice and the ‘inclusion’ of First Nations people in Australian Universities

2024· dissertation· en· W6906732736 on OpenAlexaboutno aff

Bibliographic record

VenueApollo (University of Cambridge) · 2024
Typedissertation
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousPoliticsEconomic JusticeIndigenous educationNeoliberalism (international relations)Inclusion (mineral)Social justice

Abstract

fetched live from OpenAlex

In the country now known as Australia, First Nations peoples’ participation in higher education has gained increasing policy attention since the introduction of the National Aboriginal and Torres Strait Islander Education Policy (NATSIEP) in 1989. The main focus of this policy, and allied research, has been on Indigenous peoples’ rates of access, retention and completion (ARC) in Australian universities. This research takes a different point of departure. It situates Indigenous peoples’ inclusion in higher education over this period within a wider contemporary political and socio-economic landscape which has broadly been framed by neoliberalism as a political project. In short, I ask: what is it that Indigenous students have access to, and does it provide a promising avenue for social justice? Guiding this project, from a methodological and ethical stance, is Indigenous Standpoint Theory, the Cultural Interface (Nakata 2007) and Critical Indigenous Studies. I also engage with critical statistical studies, critical policy studies on governing, and social justice theories. To answer the research question above, I added an additional set of questions to probe what has occurred for Indigenous student participation since the introduction of NATSIEP? First, what do the statistics on Indigenous peoples’ participation in university tell us? Second, what are the main policy (especially funding) mechanisms influencing universities and how do these impact Indigenous peoples’ participation in Australian universities? Third, how might we assess these policies, their priorities, and the story that the data tells us from a social justice point of view? The thesis draws on multiple methods and sources, including the careful analysis of government statistics, official government reports and interviews with senior Indigenous leaders in universities. The findings reveal, first, that much of the data that is reported over time, pertaining to Indigenous student inclusion in Australian universities is incomplete and that the underpinning assumptions shift with politics. Moreover, further scrutiny reveals a story of declining rates of Indigenous students ARC, relative to the overall cohort of students in Australian universities. Secondly, that there is inequality between universities. Indigenous students have access to very different experiences and resources depending on the university, with the top universities using the institutional finances as augmenting rather than the main source of funds. Thirdly, that the funding mechanisms aimed to include Indigenous students are insufficient and many Indigenous students leave university without a degree but with a debt. I use different framings of social justice – from Fourcade’s (2017) concept of ‘statistical fairness’ to Streeck’s (2014) ‘market justice’, to argue that neither of these are an adequate account of social justice for Indigenous peoples. I call into question the current inclusion agenda and argue that it is a mere hollow, performative agenda, that has neglected to adequately attend to the stolen land Australian universities are built on. Instead, I argue that reparative justice is needed, not only to attend to the past but also anchored in the future. I offer recommendations regarding what this might look like in policy. Further research is needed on how we, as Indigenous people, centre our own agendas in the university that are based on principles of self-determination.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.618
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0060.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.007
GPT teacher head0.264
Teacher spread0.258 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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