Bibliographic record
Abstract
Abstract This article is an appreciative exegesis of Steven Burns's article “If a Lion Could Talk.” In his essay, Burns clarifies Ludwig Wittgenstein's enigmatic remark “If a lion could talk, we wouldn't be able to understand him” by locating it within the broader context of Wittgenstein's work in the philosophy of psychology. We read Burns's interpretation of the remark as opening core Wittgensteinian issues of meaning and (mis)understanding, and we situate it within the context of the work of Burns's teacher, Peter Winch. Our discussion is a close exegesis of the immediate content of the lion remark and it highlights connections to Wittgenstein's remarks on James George Frazer's The Golden Bough. We show how Burns and Winch employ Wittgenstein's methods of dissolving philosophical puzzles by drawing attention to intermediate familiar cases. We conclude with some impressionistic remarks about Socrates in a short discussion of the difficulty of the philosophical technique and activity Burns demonstrates and recommends.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.026 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.027 | 0.005 |
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".