Bibliographic record
Abstract
In his engaging and provocative paper, John Covaleskie draws our attention to the question of how moral communities that value tolerance ought to respond when members of their own community commit an intolerable act.I am grateful for Covaleskie's emphasis on moral communities, instead of the more common focus on individual moral development, agency, virtues, or moral reasoning.In what follows, however, it will become clear that Covaleskie and I hold quite different views on the moral and educative worth of University of Oklahoma President David Boren's response to the Sigma Alpha Epsilon (SAE) fraternity members who had been filmed participating in racist chants. Actions And consequencesAs Covaleskie recounts, the day after the video became public, Boren issued a statement in which he condemned the SAE fraternity members: "I have a message for you," he said."You are disgraceful.You have violated all that we stand for.You should not have the privilege of calling yourselves 'Sooners.'Real Sooners are not racist.Real Sooners are not bigots…," and so on.2 Boren swiftly expelled Levi Pettit and Parker Rice (the two members of the SAE fraternity who were easily identifiable on the video), disbanded the fraternity chapter, and ejected SAE members from their frat house.Covaleskie characterizes Boren's response as "public moral education," and argues that his actions were "not only justified, they were morally exemplary, and they provide us with an example of moral pedagogy."3
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.044 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".