Generic Language for Social and Animal Kinds: An Examination of the Asymmetry Between Acceptance and Inferences
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.
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 | 0.012 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".