Bibliographic record
Abstract
Teachers engaged in moral education face a puzzle. We aim to bring children up to believe in and subscribe to basic moral standards such as prohibitions against harming others and requirements to help when we can. At the same time, there is widespread reasonable disagreement about the content and justification of morality, and teaching standards as justified when there is reasonable disagreement is wrongfully indoctrinatory. I analyze two answers to the puzzle, posed by Hand (2018a) and White (2016), defending White’s education in altruism approach against Hand’s criticisms by drawing from an analogy with the teaching of standards and principles in other subjects. Moral standards need not be metaethically justified to children all the way down, like how we need not teach children the metaphysical foundations of math and science in order to teach the standards of math and science in a way that does not wrongfully indoctrinate them. I conclude in favor of a pluralistic approach to teaching the reasons to abide by basic moral standards.
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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.040 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".