The Brilliance–Belonging Model: How Cultural Beliefs About Intellectual Ability Undermine Educational Equity
Bibliographic record
Abstract
As societies worldwide grapple with substantial educational inequities, understanding their underlying causes remains a priority. Here, we introduce the Brilliance-Belonging Model, a novel theoretical framework that illuminates how cultural beliefs about exceptional intellectual ability create inequities through their impact on students' sense of belonging. The model identifies two types of widespread cultural beliefs about ability: field-specific ability beliefs (FABs) and brilliance stereotypes. FABs are cultural beliefs about the extent to which success in an educational context requires exceptional intellectual ability or "brilliance" (e.g., math more so than language). In contrast, brilliance stereotypes are cultural beliefs that associate exceptional intellectual ability with some groups more than others (e.g., individuals from high vs. low socioeconomic status backgrounds). According to the Brilliance-Belonging Model, students from groups targeted by negative brilliance stereotypes are perceived-by themselves and others-as not belonging in contexts where brilliance-oriented FABs are common. These perceptions compromise students' psychological safety and lead to disempowering treatment by others, resulting in persistent gaps in achievement and representation. Such effects are amplified by the competitive climates to which brilliance-oriented FABs give rise, where pressure to demonstrate intellectual superiority creates particular challenges for students from intellectually stigmatized groups, who often value cooperation over competition. By revealing how cultural beliefs about intellectual ability shape educational outcomes through their effects on belonging, the Brilliance-Belonging Model provides a roadmap for interventions aimed at fostering a sustained sense of belonging among diverse students.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".