Bibliographic record
Abstract
Abstract Oppressive ideology regularly misrepresents features of structural injustice as normal or appropriate. I argue that resisting such injustice therefore requires critical examination of the evaluative judgments encoded in shared concepts. I diagnose a mechanism of ideological misevaluation, which I call hermeneutical misfit. Hermeneutical misfit occurs when thick concepts, or concepts which both describe and evaluate, mobilise ideologically warped evaluative judgments which do not fit the facts (e.g. slutty). These ill-fitted thick concepts in turn are regularly deployed as if they merely describe (hence ‘just the facts’). I argue that, via this descriptive masquerade, ill-fitted thick concepts smuggle in warped evaluative judgments alongside apparently value-neutral ‘mere facts’, a process which both reinforces those judgments and increases the difficulty of critique. I suggest that, to resist this process, we should develop collective consciousness and articulate ‘meta-hermeneutical resources,’ or thick concepts which encode critique of other, ill-fitted concepts (e.g. slut-shaming).
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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 | 0.025 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.099 |
| Scholarly communication | 0.011 | 0.017 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.007 |
| 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".