Using Alain Badiou's Ethic of Truths to Support an 'Eventful' Social Justice Teacher Education Program
Bibliographic record
Abstract
In this paper, the authors articulate a social justice purpose for an undergraduate teacher education program at a major Canadian university that they hope will inform more general discussions about social justice and teaching. The thesis they explore is the necessity for social justice teaching to utilize the acquisition of subject matter as a means to examine the ways in which our individual "nexus of privilege and ignorance reflect broader struggles over purposes and identities around which social practices such as education coalesce. After developing this thesis in relation to relevant literature, they turn to Henderson and Gornik's (2007) paradigmatic interpretation of "curriculum wisdom" and Badiou's (2000, 2001, 2003) ethical subjectivity of "truth-processes" to explore what the organization of curriculum in support of social justice might look like.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.035 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".