Examining the effects of descriptive norms on muscular endurance: Gender effects
Bibliographic record
Abstract
Using normative messages to influence behaviour has been receiving increased attention in the activity setting, with descriptive norms (DN; perception of what is commonly done) being associated with various forms of activity-related behaviour (Priebe & Spink, 2015). While the results have demonstrated a positive relationship for the most part, studies have yet to examine the effects of gender. This is surprising given that studies in other areas have found that the salience of norms on behaviour differs by gender (Elek et al., 2006). We explored the moderating effect of gender on the relationship between DN and muscular endurance in undergraduates. University students (N = 35) were randomly assigned to one of two conditions: control (nmale=10, nfemale=8) or DN (nmale=9, nfemale=8), and asked to perform two plank exercises to maximal exertion. Between planks, those in the DN condition were given a message that most others (similar to them) held their second plank 20% longer. Controlling for the initial plank time, ANCOVA results revealed a significant interaction effect, ηp2 = 0.12, indicating a strong effect. Means revealed that females in the descriptive norm condition held their second plank significantly longer than those in the control condition (adj. Cohen's d = 1.12), whereas there were no differences in plank hold times for males. If replicated, these results suggest that the effects of descriptive norms may differ between genders with females more likely than males to increase muscular endurance if they perceive more of their peers exhibiting a maximal effort on a similar task.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".