Language Signaling High Proportions and Generics Lead to Generalizing, but Not Essentializing, for Novel Social Kinds
Bibliographic record
Abstract
Generics (e.g., "Dogs bark") are thought by many to lead to essentializing: to assuming that members of the same category share an internal property that causally grounds shared behaviors and traits, even without evidence of such a shared property. Similarly, generics are thought to increase generalizing, that is, attributing properties to other members of the same group given evidence that some members of the group have the property. However, it is not clear from past research what underlies the capacity of generic language to increase essentializing and generalizing. Is it specific to generics, or are there broader mechanisms at work, such as the fact that generics are terms that signal high proportions? Study 1 (100 5-6 year-olds, 140 adults) found that neither generics, nor high-proportion quantifiers ("most," "many") elicited essentializing about a novel social kind (Zarpies). However, both generics and high-proportion quantifiers led adults and, to a lesser extent, children, to generalize, with high-proportion quantifiers doing so more than generics for adults. Specifics ("this") did not protect against either essentializing or generalizing when compared to the quantifier "some." Study 2 (100 5-6 year-olds, 112 adults) found that neither generics nor visual imagery signaling high proportions led to essentializing. While generics increased generalizing compared to specifics and visual imagery signaling both low and high proportions for adults, there was no difference in generalizing for children. Our findings suggest high-proportion quantifiers, including generics, lead adults, and to some extent children, to generalize, but not essentialize, about novel social kinds.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".