How essentialist reasoning about language acquisition relates to educational myths and policy endorsements
Bibliographic record
Abstract
How people conceptualize learning is related to real-world educational consequences across many domains of education. Despite its centrality to the educational system, we know little about how the public reasons about language acquisition, and the potential consequences for their thinking about real-world issues (e.g., policy endorsements). The current studies examined people's essentialist beliefs about language acquisition (e.g., that language is innate and biologically based), then investigated how individual differences in these beliefs related to the endorsement of educational myths and policies. We probed several dimensions of essentialist beliefs, including that language acquisition is innate, genetically based, and wired in the brain. In two studies, we tested specific hypotheses regarding the extent to which people use essentialist thinking when reasoning about: learning a specific language (e.g., Korean), learning a first language more generally, and learning two or more languages. Across studies, participants were more likely to essentialize the ability to learn multiple languages than one's first language, and more likely to essentialize the learning of multiple languages and one's first language than the learning of a particular language. We also found substantial individual differences in the degree to which participants essentialized language acquisition. In both studies, these individual differences correlated with an endorsement of language-related educational neuromyths (Study 1 and pre-registered Study 2), and rejection of educational policies that promote multilingual education (Study 2). Together, these studies reveal the complexity of how people reason about language acquisition and its corresponding educational consequences.
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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 | 0.028 | 0.092 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.019 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".