Cold Case: Reopening the File on Tolerance in Teaching and Learning Across Difference
Bibliographic record
Abstract
Although tolerance was long considered a core virtue for citizenship in a pluralist democracy, it has recently fallen out of favour.In a nutshell, tolerance suggests having to put up with something (or some person or idea) that one would really rather not have to put up with.And while tolerance is obviously better than hatred or overt discrimination, it is, at best, a minimal virtue.Tolerance neither implies nor requires a commitment to social justice, to questioning the status quo, understanding, or having genuine respect for the other.Therefore, in societies committed to the ideals of participatory democracy with equality and justice for all, tolerance simply does not suffice as either a political or moral virtue.However, I wonder whether there are situations -specifically in teaching and learning across difference -in which tolerance might serve a strategic function not as readily achieved by other means.Let me begin by recounting an experience from my own practice that forced me to challenge my understanding of what it means to "meet the other morally" in pedagogical relations marked by radical difference. 1
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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 | 0.032 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.074 |
| Scholarly communication | 0.023 | 0.049 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.024 | 0.053 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".