Choosing to lose it: the role of autonomous motivation in goal disengagement.
Bibliographic record
Abstract
When people hit roadblocks with their personal goals, goal disengagement is an adaptive response associated with improved mental and physical health. However, people can have trouble letting go of goals, even when pursuing them is problematic. We introduce a motivational model of goal disengagement by proposing that having autonomous motivation to disengage (a sense of truly identifying with the decision) as opposed to controlled motivation to disengage (feeling forced to let go) allows for people to make greater progress disengaging from specific goals, and prevents people getting stuck in an “inaction crisis” where they feel torn between disengaging further or re-adopting the goal. Using prospective longitudinal designs, we tracked the goal disengagement of personal goals in university students (Study 1, N = 510) and a general adult sample of Americans (Study 2, N= 446), finding that autonomous motivation for goal disengagement facilitated making disengagement progress. This work expands our understanding of the role of autonomous motivation throughout a goal’s lifecycle and helps integrate different theoretical frameworks on goal motivation and self-regulation.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".