Generic Language for Social and Animal Kinds: An Examination of the Asymmetry Between Acceptance and Inferences
Why this work is in the frame
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Bibliographic record
Abstract
Generics (e.g., "Ravens are black") express generalizations about categories or their members. Previous research found that generics about animals are interpreted as broadly true of members of a kind, yet also accepted based on minimal evidence. This asymmetry is important for suggesting a mechanism by which unfounded generalizations may flourish; yet, little is known whether this finding extends to generics about groups of people (heretofore, "social generics"). Accordingly, in four preregistered studies (n = 665), we tested for an inferential asymmetry for generics regarding novel groups of animals versus people. Participants were randomly assigned to either an Implied Prevalence task (given a generic, asked to estimate the prevalence of a property) or a Truth-Conditions task (given prevalence information, asked whether a generic was true or false). A generic asymmetry was found in both domains, at equivalent levels. The asymmetry also extended to properties varying in valence (dangerous and neutral). Finally, there were differences as a function of property valence in the Implied Prevalence task and a small but consistent interaction between domain and prevalence in the Truth-Conditions task. We discuss the implications of these results for the semantics of generics, theoretical accounts of the asymmetry, and the relation between generics and stereotyping.
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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 | 0.001 | 0.001 |
| 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