The Effectiveness of Associative and Rational Statistical Learning in Reducing Children’s Stereotype Formation
Bibliographic record
Abstract
A stereotype is a rigid and overgeneralized belief about the characteristics of a social group. Stereotyping is a pervasive phenomenon, and has detrimental effects on children’s development such that it leads to biased information processing and stereotype threat. One of the underlying mechanisms for stereotype formation is illusory correlation, which refers to the erroneous inference about the relationship between two categories of events that in fact are uncorrelated. Given that most of the stereotype reduction training is focused on adults rather than children, this Master’s thesis aimed to examine the effectiveness of two methods that could potentially reduce stereotyping in children. More specifically, this work investigated whether facilitating associative and rational statistical learning could reduce stereotyping in children through inhibiting the formation of illusory correlation. The results showed that 5- to 10-year-old children consistently perceive an illusory correlation between the numerically smaller minority group and the infrequently occurring, negative behaviour. However, the perception of an illusory correlation among 5- to 8-year-olds was significantly reduced through the facilitation of statistical learning, but not associative learning.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".