Social norms and activity: Examining salience
Bibliographic record
Abstract
Recent research examining social norms and activity behaviour has used the focus theory of normative conduct as its theoretical underpinning (Cialdini et al., 1990). While relationships have emerged (Priebe & Spink, 2011), one of the main postulates of the theory has yet to be tested - that norms will only influence behaviour when the norm is salient to the individual. This study aimed to examine the impact of norms on effort in an activity task where salience for activity differed (Kinesiology/non-Kinesiology students). Participants were assigned to one of two conditions: control (n=12) or normative (n=13), and then asked to perform two activity tasks (planks) to maximum exertion separated by a 3-minute rest. During the rest, participants in the normative condition were told, “80% of similar others believe that individuals should be able to hold their second plank 20% longer, and 80% of these people held their second plank 20% longer”; control received no message. A mixed-design ANCOVA (controlling for time-1 plank) revealed differences between conditions in time-2 plank hold (p < .001); however, this was qualified by a significant condition/group interaction (p = .04) where differences between conditions only emerged for the Kinesiology students (p < .001). The finding that normative messages elicit a larger behaviour change than when no message is presented is consistent with past activity research (Priebe et al., 2013). However, the fact that this effect only occurred for Kinesiology students suggests that messages might only be effective when the message is salient to the individual.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".