Where Do Self-Concordant Goals Come From? The Role of Domain-Specific Psychological Need Satisfaction
Bibliographic record
Abstract
Previous research has shown that self-concordant goals are more likely to be attained. But what leads someone to adopt a self-concordant goal in the first place? The present research addresses this question by looking at the domains in which goals are set, focusing on the amount of psychological need satisfaction experienced in these domains. Across three experimental studies, we demonstrate that domain-related need satisfaction predicts the extent to which people adopt self-concordant goals in a given domain, laying the foundation for successful goal pursuit. In addition, we show that need satisfaction influences goal self-concordance because in need-satisfying domains people are both more likely to choose the most self-concordant goal (among a set of comparable choices), and are more likely to internalize the possible goals. The implications of this research for goal setting and pursuit as well as for the importance of examining goals within their broader motivational framework are discussed.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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 teacher head, 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".