The differential effects of intrinsic and identified motivation on well-being and performance: Prospective, experimental, and implicit approaches to self-determination theory.
Bibliographic record
Abstract
Self-determination theory research has demonstrated that intrinsic and identified self-regulations are associated with successful adaptation. However, few distinctions are typically made between these regulations and their outcomes. In the present studies, the associations between intrinsic and identified motivations and outcomes of psychological well-being and academic performance are compared in educational settings. In Study 1, intrinsic self-regulation predicted psychological well-being, independent of academic performance. In contrast, identified regulation predicted academic performance. Additionally, the more that students demonstrated an identified academic regulation, the more that their psychological well-being was contingent on performance. In Study 2a, priming intrinsic self-regulation led to greater psychological well-being 10 days later. In Study 2b, an implicit measure of identified regulation predicted academic performance 6 weeks later. Results indicate the need to address important distinctions between intrinsic and identified regulations.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".