Fixed Intelligence Mindset, Self-Esteem, and Failure-Related Negative Emotions: A Cross-Cultural Mediation Model
Bibliographic record
Abstract
A growing body of literature supports that fixed intelligence mindset promotes the emergence of maladaptive emotional reactions, especially when self-threat is imminent. Previous studies have confirmed that in adverse academic situations, students endorsing fixed intelligence mindset experience higher levels of negative emotions, although little is known about the mechanisms through which fixed intelligence mindset exerts its influence. Thus, the present study ( N total = 398) proposed to investigate self-esteem as a mediator of this relationship in two different cultural contexts, in Hungary and the United States. Structural equation modeling revealed that self-esteem fully mediated the relationship between fixed intelligence mindset and negative emotions. Furthermore, results of the invariance testing conferred preliminary evidence for the cross-cultural validity of the mediation model. These findings suggest that, students adhering to fixed intelligence beliefs tend to experience greater self-esteem loss when experiencing academic failure, which leads to higher levels of negative emotions.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".