Bibliographic record
Abstract
In this paper, I discuss the Ontario College of Teachers’ most recent versions of the Standards of Practice with William Hare’s counsel on being open-minded regarding open-mindedness in mind. Specifically, I insist that the use of the Standards of Practice as guidelines for working through cases of professional and ethical issues requires yet another rule to indicate when to deviate from this or that standard. In this way, open-mindedness consists of developing and following rules to indicate when and where specific standards should be bypassed. These rules vary, however, one source of these can be found in what Barbara Herman has called, “Rules of Moral Salience”—rules that guide us in our day-to-day moral decision-making and that we draw on when called upon to make moral-ethical judgments. What this means for various ethics (ethics of care; Kantian-type ethics, psychological and/or developmental accounts of ethics) is also broached.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.119 | 0.173 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.103 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.014 | 0.017 |
| Insufficient payload (model declined to judge) | 0.002 | 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".