Who's in and who's out of the cognitive kinding game? Comments on Muhammad Ali Khalidi's <i>Cognitive ontology: Taxonomic practices in the mind‐brain sciences</i>
Bibliographic record
Abstract
Muhammad Ali Khalidi contends that because cognitive science casts a wider net than neuroscience in searching for the causes of cognition, it is in the superior position to discover “real” cognitive kinds. I argue that while Khalidi identifies appropriate norms for individuating cognitive kinds, these norms ground his characterization of taxonomic practices in cognitive science, rather than the other way around. If we instead treat Khalidi's norms not as descriptively accurate characterizations of taxonomic practices in cognitive science, but as a set of best practices for kinding cognition, is cognitive science in and neuroscience definitively out of the cognitive kinding game?
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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 | 0.013 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.016 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.027 | 0.038 |
| Insufficient payload (model declined to judge) | 0.006 | 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".