Bibliographic record
Abstract
In most everyday instances of reasoning, reasoners can gain, lose, and reacquire entitlement to (or justification for) a possible commitment (or belief) as a result of their consecutively acquiring new commitments. For example, we might initially conclude that ‘Tweety can fly’ from ‘Tweety is a bird,’ but later have to reject this conclusion as a result of our coming to learn that Tweety is a penguin. We could, even later, reacquire entitlement to ‘Tweety can fly’ if we became committed (and presumably entitled) to the claim ‘Tweety has a jetpack.’ I will call this very common feature of reasoning entitlement recovery. In this paper I will argue that the types of inferential relations that are central to Brandom’s entire account of language and reasoning make entitlement recovery impossible. I will then briefly attempt to diagnose why this problem arises for Brandom and suggest how his account should be modified so that it will successfully allow entitlement recovery.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".