Bibliographic record
Abstract
Haslanger presents social meanings as cultural tools for social coordination. One of their main features is ‘naturalness’ of use in cognition and practice. Some cultural tools undergird unjust social practices, in which case they constitute an ideology. In my commentary, I wish to investigate the notion of cultural tool, and consider how the break-down of these tools is often a pre-requisite for conducting ideology critique. I take naturalness to be a quality possessed by social meanings that consists in a) their taken-for-granted character as unarticulated significance-granting entities; and b) their unquestioned character. When these features are lost to a member or a group of society, we have what I call their cognitive estrangement. Estrangement consists in two processes that ‘de-naturalize’ social meanings: first, the articulation of previously inarticulate social meanings, and, second, their emergence as something that is earnestly interrogated. Articulation and earnest interrogation are necessary steps in developing an ideology critique. I contend, further, that estrangement can itself be a social practice. In the latter case both orthodox and heterodox groups participate in estrangement, since–in as much as both interact with each other–both are exposed to the earnest interrogation of hitherto taken-for-granted social meanings. I consider some consequences of this perspective in situations where, due to marginalization, the discursive resources for articulation have themselves been unjustly limited.
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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 | 0.016 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.108 |
| Scholarly communication | 0.013 | 0.020 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 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".