Education and the Politics of Difference: Iris Young and the politics of education
Bibliographic record
Abstract
Three key contributions of Iris Young to democratic political theory, and three challenges that have arisen in response to Young's theory, are examined here in relation to education. First, Young has argued that oppression and domination, not distributive inequality, ought to guide discussions about justice. Second, eliminating oppression requires establishing a politics that welcomes difference by dismantling and reforming structures, processes, concepts and categories that sustain difference‐blind, impartial, neutral, universal politics and policies. The infatuation with merit and standardized tests, both of which are central to measuring educational achievement, are chief amongst the targets in need of reform. Third, a politics of difference requires restructuring the division of labour and decision‐making so as to include disadvantaged social groups but allow them to contribute without foregoing their particularities. The challenges that have arisen in response to Young's theory are first, that difference is merely another way of getting at inequality of resources or opportunities, and if it is not, then, second, a politics of difference values difference for the sake of difference rather than for the sake of alleviating social disadvantage. Third, in theory and in practice a politics that focuses on difference putatively jeopardizes a politics whose aim is to improve the redistribution of resources.
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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 | 0.010 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.039 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.007 |
| 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".