Conceptual engineering, cognitive deficiency, and the foundations of conceptual inquiry
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.013 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it