An integrative analytical framework for understanding the effects of autonomous and controlled motivation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
To use polynomial regression analysis with response surface methodology to examine the extent to which autonomous motivation (AM) and controlled motivation (CM) as separate constructs, as well as how the degree of agreement/differentiation and the direction of differentiation among them, can predict outcomes in academic and health contexts. Data from two studies with university students and one study with breast cancer survivors were used. In general, AM predicted positive academic and health outcomes, whereas CM positively predicted negative outcomes. Positive outcomes were generally higher whereas negative outcomes were generally lower when AM was greater than CM and when agreement between AM and CM increased. Consideration of the degree of agreement and the direction of differentiation between AM and CM adds to the interpretation of the associations between motivation and outcomes in academic and health contexts that is not captured by simply examining AM or CM separately or using a combined AM–CM score.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it