You talkin' to me: Effects of descriptive norms on muscular endurance as moderated by exercise identity
Bibliographic record
Abstract
Descriptive norms (DN; perception of what is commonly done) have been associated with activity (Crozier & Spink, 2017; Priebe & Spink, 2015), with most studies using focus theory of normative conduct (Cialdini et al., 1990) as their theoretical underpinning. One of its main postulates states that individuals are more likely to act on descriptive norm information when it is salient to them. The purpose was to examine the impact of descriptive norms on muscular endurance in a exercise where salience for the exercise behaviour differed (high exercise identity (HEXID)/low exercise identity (LEXID)). It was hypothesized that only HEXID individuals who received a descriptive norm would hold their longer than those who did not receive a message. Undergraduate students were randomly assigned to one of two conditions: descriptive norm (DN, n=31) and control (C, n=32), and then asked to perform two planks to maximum exertion separated by a 3-min rest. After completing the first, DN participants received a norm-specific message that 80% of university students held their second 20% longer than their first plank while C participants received no message. Exercise identity also was assessed. A mean split of the exercise identity item was used to create the HEXID and LEXID groups. ANCOVA results (controlling for time 1 plank) revealed a significant condition/identity group interaction (p=.039). Supporting the hypothesis, significant differences between conditions only emerged for HEXID individuals with those receiving the message holding their second longer (M=98.5 sec) than those not receiving the message (M=80.4 sec).
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".