Comparing individual reactions to exercise identity consistency and inconsistency: A test of Identity Theory predictions
Bibliographic record
Abstract
According to Identity Theory, individuals react differently when they perceive their behaviour to be consistent versus inconsistent with their identity. Inconsistent judgments are associated with negative affect, decreased self-efficacy and motivation for identity-relevant behaviour; consistent judgments are associated with low negative affect, enhanced self-efficacy and maintained motivation. Strength of identity influences the magnitude of reactions. This study compared individuals' reactions to judgments of exercise identity-behaviour consistency and inconsistency. Participants in this 2 (consistency judgement) x 2 (high/low exercise identity) factorial design were 79 university students who judged themselves as consistent and inconsistent with their identity at two different time points. At both times, participants completed measures of exercise identity, self-efficacy, affect, intentions for exercise, past exercise and perceptions of consistency. MANOVA procedures revealed significant judgment (p < .001) and identity main effects (p < .001). Consistency judgments were associated with greater percent consistency, past exercise, and self-efficacy for future exercise than were inconsistency judgments (p's < .04). High exercise identity individuals reported more exercise and greater self-efficacy than low identity individuals (p's
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".