Social Kinds, Conceptual Analysis, and the Operative Concept: A Reply to Haslanger
Bibliographic record
Abstract
Sally Haslanger (2006) is concerned with the debate between social constructionists and error theorists about a given category, such as race or gender. For example, social constructionists about race claim that the term “race” refers to a social kind, whereas error theorists claim that the term “race” is an empty term, that is, nothing belongs to this category. It seems that this debate depends in part on the meaning of the corresponding expression, and this, according to some theorists, depends in turn on our intuitions as competent speakers. But then, what should we say if competent users of the expressions “race” and “gender” understand the terms so that being a natural or biological property is a necessary condition in order to fall under the term? If that were the case, then it would seem that a social constructionist view would be out of the question. Haslanger (2005, 2006) has argued that a social constructionist view could still be defended in that situation. In order to argue for this, she draws on the classical arguments for semantic externalism (Putnam, 1975, Burge, 1979, Kripke, 1980), which show that the intuitions of competent speakers concerning the nature of a given category, and the objective type that actually unifies the instances of that category, may come apart. In this paper I will argue that the arguments for semantic externalism concerning natural kinds do not really offer support for Haslanger’s claim that ordinary intuitions concerning social kinds are not relevant.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.034 |
| Scholarly communication | 0.009 | 0.046 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.028 | 0.038 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".