Autonomous Motivation, Controlled Motivation, and Goal Progress
Bibliographic record
Abstract
Although the self-concordance of goals has been repeatedly shown to predict better goal progress, recent research suggests potential problems with aggregating autonomous and controlled motivations to form a summary index of self-concordance (Judge, Bono, Erez, & Locke, 2005). The purpose of the present investigation was to further examine the relations among autonomous motivation, controlled motivation, and goal progress to determine the relative importance of autonomous motivation and controlled motivation in the pursuit of personal goals. The results of three studies and a meta-analysis indicated that autonomous motivation was substantially related to goal progress whereas controlled motivation was not. Additionally, the relation of autonomous motivation to goal progress was shown to involve implementation planning. Together, the three studies highlight the importance for goal setters of having autonomous motivation and developing implementation plans, especially ones formulated in terms of approach strategies rather than avoidance strategies. The present research suggests that individuals pursuing goals should focus relatively greater attention on enhancing their autonomous motivation rather than reducing their controlled motivation.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.001 | 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".