A Cross-Cultural Investigation of Metamotivational Beliefs About Regulatory Focus Task-Motivation Fit
Bibliographic record
Abstract
Recent metamotivation research revealed that Westerners recognize that promotion versus prevention motivations benefit performance on eager versus vigilant tasks, respectively; that is, they know how to create task-motivation fit with respect to regulatory focus. Westerners also believe that, across tasks, promotion is more beneficial than prevention (i.e., a promotion bias). Adopting a cross-cultural approach, we examined whether beliefs about task-motivation fit generalize across cultures, whether Easterners exhibit a contrasting prevention bias, and the role of independence/interdependence in these beliefs. Results revealed cross-cultural similarities in metamotivational beliefs. Moreover, Easterners and Westerners alike often exhibited a promotion bias, suggesting that this effect may not be shaped by culture. One potential cultural difference did emerge: Easterners appeared to recognize how to create task-motivation fit for both independent and interdependent outcomes, whereas Westerners only recognized how to do so for independent outcomes. We discuss the role of culture in shaping metamotivation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".