DOES TASK-RELATED IDENTIFIED REGULATION MODERATE THE SOCIOMETER EFFECT? A STUDY OF PERFORMANCE FEEDBACK, PERCEIVED INCLUSION, AND STATE SELF-ESTEEM
Bibliographic record
Abstract
The aim of this study was to understand the processes explaining the effects of private performance feedback (success vs. failure) on state self-esteem from the stance of sociometer theory and self-determination theory. We investigated whether or not the effect of private performance feedback on state self-esteem was mediated by perceived inclusion as a function of participants' level of task-related identified regulation (i.e., importance of the activity for oneself). Ninety participants were randomly assigned to one of the following three conditions: failure, success, or control. Our regression analyses based on both original and bootstrap samples indicate that perceived inclusion does not mediate the effect of feedback on state self-esteem for individuals high in task-related identified regulation. Such an effect only operates for individuals low in task-related identified regulation. In sum, our results show that the perceived inclusion process proposed by sociometer theory applies more when individuals find that the activity is less important for them (i.e., identified regulation).
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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 | 0.017 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".