Bibliographic record
Abstract
My aim in the present paper is to develop a new kind of argument in support of the ideal of liberal neutrality. This argument combines some basic moral principles with a thesis about the relationship between the correct standards of justification for a belief/action and certain contextual factors. The idea is that the level of importance of what is at stake in a specific context of action determines how demanding the correct standards to justify an action based on a specific set of beliefs ought to be. In certain exceptional contexts –where the seriousness of harm in case of mistake and the level of an agent’s responsibility for the outcome of his action are specially high– a very small probability of making a mistake should be recognized as a good reason to avoid to act based on beliefs that we nonetheless affirm with a high degree of confidence and that actually justify our action in other contexts. The further steps of the argument consist in probing 1) that the fundamental state’s policies are such a case of exceptional context, 2) that perfectionist policies are the type of actions we should avoid, and 3) that policies that satisfy neutral standards of justification are not affected by the reasons which lead to reject perfectionist policies.
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 | 0.015 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.024 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.008 | 0.011 |
| 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".