Bibliographic record
Abstract
The terms of the title can be understood in various ways, along with the frameworks in which they are embedded. I would like to outline interpretations that I think are useful and proper, and to suggest a more general thesis, which would require much more comprehensive argument: that there is no coherent alternative to proceeding in this way for the range of issues addressed, and that other endeavors in roughly the same realm are clarified and facilitated if understood as extensions of the approach outlined. Deflating the terms Putting “language” aside for the moment, let's begin by taking the other terms of the title in ways that are innocent of far-reaching implications, specifically, divorced from any metaphysical connotations. Take the term “mind” or, as a preliminary, “mental.” Consider how we use such terms as “chemical,” “optical,” or “electrical.” Certain phenomena, events, processes, and states are called “chemical” (etc.), but no meta-physical divide is suggested by that usage. These are just various aspects of the world that we select as a focus of attention for the purposes of inquiry and exposition. I will understand the term “mental” in much the same way, with something like its traditional coverage, but without metaphysical import and with no suggestion that it would make any sense to try to identify the true criterion or mark of the mental.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.016 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".