Mining Aboriginal success: The politics of difference in continuing education for industry needs
Bibliographic record
Abstract
This article uses a study of a northern School of Mining to interrogate questions about the relationship being negotiated between aboriginality and neoliberalism. Aboriginal Peoples have long fought for control over education as central to their right of self‐determination. There are now many examples of programming and policies oriented to Aboriginal people in Canadian public post‐secondary institutions. Alongside these Aboriginal gains, there has been a neoliberal restructuring of post‐secondary education. This restructuring has served to open spaces to contest established colonial rationalities, technologies of power, and normed subjectivities in education, but it has also increasingly oriented schooling to economic goals, particularly skilling workers for local labour markets. Through neoliberal reforms, Aboriginal Peoples have achieved new forms of increased recognition. Elements of these changes correspond to Aboriginal Peoples’ long‐standing demands and vital aspirations. However, neoliberal governmentality continues to condition the possibilities for change. I argue the intertwining of Aboriginal self‐determination with efforts to restructure education to better serve labour markets has shaped an aporetic terrain, where neoliberalism, under the banner of social justice, has itself become the vehicle for a limited version of justice demanded by marginalized communities. This advances a partial form of recognition which necessarily leaves aspects of Aboriginal claims unanswered.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.033 | 0.024 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".