When Bigger is Better: Size of Strategy Repertoire Predicts Goal Attainment
Bibliographic record
Abstract
People are highly motivated to change their behavior. Unfortunately, many people have difficulty doing so. Building on recent theorizing, we propose that having access to a wide range of strategies – that is, a larger strategy repertoire – can help people achieve their goals. In eight samples across a variety of domains, participants (Ntotal=2,347) reported their use of seven strategies that determined their strategy repertoire for a particular goal. Findings indicated that having a larger strategy repertoire predicted goal attainment. These findings replicated across domains (healthy eating, academic performance, saving money), among self-set personal goals, at both the between-person and within-person levels, and were robust to different operationalizations of strategy repertoire, but differed by behavior. Specifically, having a larger strategy repertoire consistently predicted subjective goal progress, healthier eating, and more adaptive financial behaviors, but not snack intake or subjective credit score. Among the people who reported on multiple goals, we also found that their strategy repertoire was highly similar across domains. Together, these findings highlight that having a larger strategy “toolbox” has important implications for downstream regulation processes, which sets the stage for lasting behavior change.
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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 | 0.001 | 0.008 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".