Social Mobility and Motivational Payoff: Achievement Motivation Is More Important in Students’ Performance and Well-Being in Cultures With High Versus Low Social Mobility
Bibliographic record
Abstract
Achievement motivation encompasses a well-establish distinction between the motive to avoid failure (e.g., fear of failure) and the orientation to improve competence (e.g., mastery goal). But how well do they generalize across cultures in understanding students’ performance and well-being? We argue that students’ achievement motivation is less pronounced in societies characterized by low (vs. high) social mobility, where people have fewer opportunities to change their social status. To test this hypothesis, we analyzed a cross-national data set ( n = 498,362 high-school students from 65 regions) using multilevel modeling. The results indicated that societal-level social mobility significantly moderated the role of (a) mastery goals and fear of failure on academic performance and (b) fear of failure on well-being. These associations were stronger in societies with high (vs. low) social mobility, suggesting that students derive greater academic benefits from mastery goals and fear of failure in societies with higher social mobility. In such societies, however, fear of failure also poses a stronger hindrance to students’ well-being. These findings highlight that the same type of motivation may operate differently across cultures and that socioecological environments may influence the motivational impacts on learning and well-being.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".