Conceptual engineering, cognitive deficiency, and the foundations of conceptual inquiry
Bibliographic record
Abstract
As usually understood, ‘conceptual engineering’ is a form of conceptual inquiry aimed at diagnosing problems with extant concepts and finding better concepts to replace them. This can seem like an appropriate response to a skeptical concern that our concepts are cognitively deficient: unsuitable for use in serious inquiry. We argue, however, that conceptual engineering, so understood, cannot reasonably be motivated in this way. The basic problem is that on the first hand, since conceptual engineering is itself a form of inquiry, it cannot succeed by using the problematic concept itself in inquiry (since it is unsuitable for use in inquiry); but, on the other hand, methods for carrying out inquiry directed at concepts without using those concepts are constrained in such a way as to make conceptual engineering very unlikely to succeed. The upshot is that conceptual engineering has no reasonable chance of addressing the skeptical concern about cognitive deficiency. This is an important and previously unarticulated result, about what conceptual engineering can and cannot reasonably be expected to do.
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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 | 0.041 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.005 | 0.133 |
| Scholarly communication | 0.015 | 0.026 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".