We may not know what we want, but do we know what we need? Examining the ability to forecast need satisfaction in goal pursuit
Bibliographic record
Abstract
Do we have the necessary perceptual abilities to set goals that are congruent with our own values and needs? In a prospective study, participants (n=185) identified three goals that they planned to pursue throughout the week. For each goal they then rated their motivation for pursuing it and made predictions about the extent to which goal attainment would satisfy their needs for autonomy, competence, and relatedness. One week later, participants rated their progress on each goal, as well as the actual need satisfaction they experienced. Using Bayesian analysis, we found support for our (null) hypothesis that participants predicted that their goals would satisfy their basic needs, irrespective of goal self-concordance. While people sometimes overestimated their need forecasts, we found that people who pursued more self-concordant goals actually benefited more from their pursuits, both compared to others who pursued less concordant goals and among their own goals.
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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 | 0.005 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".