Self-perceptions as mechanisms of achievement inequality: evidence across 70 countries
Bibliographic record
Abstract
Abstract Children from lower socioeconomic status (SES) backgrounds tend to have more negative self-perceptions. More negative self-perceptions are often related to lower academic achievement. Linking these findings, we asked: Do children’s self-perceptions help explain socioeconomic disparities in academic achievement around the world? We addressed this question using data from the 2018 Programme for International Student Assessment (PISA) survey, including n = 520,729 records of 15-year-old students from 70 countries. We studied five self-perceptions (self-perceived competency, self-efficacy, growth mindset, sense of belonging, and fear of failure) and assessed academic achievement in terms of reading achievement. As predicted, across countries, children’s self-perceptions jointly and separately partially mediated the association between socioeconomic status and reading achievement, explaining additional 11% (Δ R 2 = 0.105) of the variance in reading achievement. The positive mediation effect of self-perceived competency was more pronounced in countries with higher social mobility, indicating the importance of environments that “afford” the use of beneficial self-perceptions. While the results tentatively suggest self-perceptions, in general, to be an important lever to address inequality, interventions targeting self-perceived competency might be particularly effective in counteracting educational inequalities in countries with higher social mobility.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".