Activating social norms: Examining the effects of injunctive, aligned and misaligned norm messages on activity behaviour
Bibliographic record
Abstract
Two common types of social norms are descriptive (what others do) and injunctive (what ought to be done; Cialdini et al., 1990). While descriptive norms have been related to activity (Priebe & Spink, 2011), injunctive norms have received less attention. The first purpose of this study examined whether injunctive norm messages would influence activity behaviour more than no message. Given the existence of these two norm types and the prevalence of mismatched messages in practice (e.g., “you should be active but the majority are not”), another question concerns the effects on behaviour when norm messages are aligned (matched injunctive/descriptive) vs. misaligned (mismatched injunctive/descriptive), which formed the second purpose. Participants were assigned to either an injunctive (n=11), aligned (n=12), misaligned (n=12) or control (n=12) condition, then performed two maximum-effort planks. Between planks, injunctive were given a message that most others thought they should hold their second plank longer; aligned received the same message as well as one stating most others actually held their second plank longer; misaligned received the same injunctive message but were told most others did not hold their second plank longer; control received no message. ANCOVA (controlling for plank 1) and post hoc results revealed that injunctive did not differ from control (p>.10) while those in the aligned condition held their second plank longer than all other conditions (p’s<.05). Findings suggest that injunctive norms might not be powerful enough to impact change in activity and aligned norm messages might be more effective than misaligned messages.Acknowledgments: 1st author supported by a Social Sciences and Humanities Research Council of Canada Vanier Graduate Scholarship.
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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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".