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Enregistrement 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 sur OpenAlexaboutno aff

Notice bibliographique

RevueApollo (University of Cambridge) · 2024
Typedissertation
Langueen
DomaineSocial Sciences
ThématiqueIndigenous Health, Education, and Rights
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésIndigenousPoliticsEconomic JusticeIndigenous educationNeoliberalism (international relations)Inclusion (mineral)Social justice

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesÉtudes des sciences et des technologies
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,618
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0060,001
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,007
Tête enseignante GPT0,264
Écart entre enseignants0,258 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeQualitatif
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2024
Routes d'admission1
Résumé présentoui

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