Bibliographic record
Abstract
In Making It Explicit (1994) Robert Brandom claims that we may distinguish those linguistic expressions with object-representational purport — the singular terms — from others merely by the structure of their inferential relations. A good part of his inferentialist program rests on this claim. At first blush it can seem implausible: linguistic expressions stand in inferential relations to each other, so how could we appeal to those relations to decide on the obtaining of what seems to be relation between linguistic expressions and objects in general (viz., x purports to represent y)? It is perhaps not surprising then that Brandom's proposal fails. But it definitely is surprising how it fails. The problem is that in order to specify the sort of generality there is to an expression's inferential role, one must appeal to some version of the traditional distinction between extensional and nonextensional occurrences of expressions, and there appears to be no way to draw anything like that distinction in inferentialist terms. For the inferential proprieties governing the different occurrences an expression can have are so varied that they do not determine a binary partition of those occurrences.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.042 |
| Scholarly communication | 0.007 | 0.015 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.007 |
| 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".