The “problem” of Australian First Nations doctoral education: a policy analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose Social marketing and government policy are intertwined. Despite this, policy analysis by social marketers is rare. This paper aims to address the dearth of policy analysis in social marketing and introduce and model a methodology grounded in Indigenous knowledge and from an Indigenous standpoint. In Australia, a minuscule number of First Nations people complete doctoral degrees. The most recent, major policy review, the Australian Council of Learned Academies (ACOLA) Report, made a series of recommendations, with some drawn from countries that have successfully uplifted Indigenous doctoral candidates’ success. This paper “speaks back” to the ACOLA Report. Design/methodology/approach This paper subjects the ACOLA Report, implementation plans and evaluations to a detailed Indigenous Critical Discourse Analysis using Nakata’s Indigenous standpoint theory and Bacchi’s Foucauldian discourse analysis to trace why policy borrowing from other countries is challenging if other elements of the political, social and cultural landscape are fundamentally unsupportive of reforms. Findings This paper makes arguments about the effects produced by the way the “problem” of First Nations doctoral education has been represented in this suite of Australian policy documents and the ways in which changes could be made that would actually address the pressing need for First Nations doctoral success in Australia. Originality/value Conducting policy analysis benefits social marketers in many ways, helping to navigate policy complexities and advocate for meaningful policy reforms for a social cause. This paper aims to spark more social marketing policy analysis and introduces a methodology uncommon to social marketing.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.008 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it