Bibliographic record
Abstract
According to a widespread reading of Philosophical Investigations, Wittgenstein’s conception of language relies on a substantial account of how language is actually learned by children.1 Originating from a set of papers written by Norman Malcolm (1954, 1982, 1989), this exegetical tradition has been prompted by several passages where Wittgenstein claims that our language is an “extension” and a “refinement” of our instinctive behaviours (PI §244, Z §454, CV p. 31, OC §538 and §204). Although Wittgenstein has always stressed the gulf lying between philosophical investigations and empirical sciences, these passages have moved a lot of scholars to draw the conclusion that Wittgenstein is actually committed to an implicit theory of language acquisition that should be taken into account in contemporary debates (HARRÉ & ROBINSON 1997, MOYAL-SHARROCK 2000). Among these ethologist readers of Wittgenstein, Canfield is probably the most radical: in a sequence of stimulating articles, he argues that ethological research should be conducted in accordance with Wittgenstein’s intuition about the learning language process (1993, 1995, 1996). This reading has, however, been seriously challenged for reasons that are mainly historical and exegetical (LOUGHLIN 2014, DROMM 2003, 2006). Among all objections encountered by the ethological reading of Wittgenstein, the most serious one is the fact that he states frequently that philosophy should not try to explain the genesis of concepts2 or to rec
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.053 |
| Scholarly communication | 0.008 | 0.029 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".