Bibliographic record
Abstract
David Gauthier tries to defend morality by showing that rational agents would choose to adopt a fundamental choice disposition that permits them to cooperate in prisoner's dilemmas. In this paper, I argue that Gauthier, rather than trying to work out a prudential justification for his favored choice disposition, should opt for a transcendental justification. I argue that the disposition in question is the product of socialization, not rational choice. However, only agents who are socialized in such a way that they acquire a disposition of this type could acquire the capacity to use language. Given the internal connection between language and thought, this means that no agent endowed with such a disposition could rationally choose to adopt another. Thus rational reflection by moral agents upon their own fundamental choice disposition will have no tendency to destabilize it. “It is a necessary truth that people tend to do what they think they ought to do, for it is a necessary truth that people who occupy a linguistic position which means /ought to do A now, tend to do A. If they did not, the position they occupy could not mean Iought to do A now.” Wilfrid Sellars, “Some Reflections on Language Games.”
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.068 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".