Bibliographic record
Abstract
Research suggests that moving synchronously with others increases social affiliation as it blurs the boundary between “self” and “other” and allows group members to focus on a shared goal. In the real world, few synchronous movement behaviours are performed without the backdrop of a musical beat to support them (i.e., tribal rituals, soldiers marching, dancing during concerts). However, to our knowledge, only one previous study examined the role of music in the association between synchronous movement and social affiliation. To examine this question, we had participants watch a 3-minute dance video in groups of 3-4. They either mimicked the dance moves in the video (moving synchronously) or simply observed the movements while seated, and music was either present or absent. As such, there were four conditions: 1) move with music, 2) move without music, 3) observe with music, 4) observe without music. Participants then completed a series of questionnaires; our dependent measures focused on social affiliation (i.e., entitativity, inclusion of other in self, trustworthiness) and prosocial behaviours (i.e., helping). We hypothesize that 1) the movement groups will show greater social affiliation and prosocial behaviour than the observation groups, and 2) the group moving to music will show the strongest effect. If hypothesis 2) is supported, we suspect that it will result from an increased mood and/or a higher degree of synchronization compared to the other group(s). Because even simple synchronous movements (e.g., finger tapping) generate feelings of community and bonding, the addition of music may enhance or exaggerate this effect. Discipline: Psychology Honours Faculty Mentor: Dr. Kathleen Corrigall
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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 | 0.001 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".