First Nations assimilation through neoliberal educational reform
Bibliographic record
Abstract
The authors undertake a geographically sensitive analysis of the influential 2010 Free to Learn report. Free to Learn proposes a reform of the funding system for First Nations students to remove funds earmarked for First Nations education from First Nations government control, convert universities into managers of First Nations student funding, and convert First Nations community members into entrepreneurs. Free to Learn employs two strategies: scalar obfuscation and the cultivation of historical geographic ignorance to obscure the links between their proposal for neoliberal educational reform and the long‐standing assimilative strategies of the Canadian and pre‐Canadian state. The authors explore the economics and demography in the report and expose its deep links to colonial and assimilative policies of the recent and distant past. They link this report to the larger theatre of education in Canada where cultures are frequently reduced to mutable symbols in an ahistorical context. They close with a reflection on the challenges and dangers of taking a political stance with limited understanding of the forces at work in the environment of neoliberal reform prevailing in Canada.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.023 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".