Metamotivational beliefs about intrinsic and extrinsic motivation.
Bibliographic record
Abstract
Although intrinsic motivation is often viewed as preferable to more extrinsic forms of motivation, there is evidence that the adaptiveness of these motivational states depends on the nature of the task being completed (e.g., Cerasoli, Nicklin, & Ford, 2014). Specifically, research suggests that intrinsic motivation tends to support better performance on open-ended tasks involving qualitative performance assessment (e.g., creative writing), while extrinsic motivation supports better performance on close-ended tasks involving quantitative performance assessment (e.g., multiple choice). This thesis examined people’s metamotivational beliefs regarding this type of task-motivation fit. Across three studies (N = 854), participants provided beliefs about the usefulness of different types of motivation-regulation strategies: strategies that enhance one’s interest and enjoyment in a task versus strategies that focus on the value associated with task outcomes (both self-relevance strategies and reward strategies). Overall, participants reported that interest-enhancing and self-relevance strategies would be more helpful for open-ended versus close-ended tasks (Studies 1, 2, and 3), whereas reward strategies would be more helpful for close-ended tasks (Studies 2 and 3). These beliefs predicted consequential behavioral choices (Study 2) and task performance (Study 3). Implications for understanding effective self-regulation are discussed.
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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 | 0.001 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.004 |
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".