Imagine running together: Preliminary experimental study of how running group membership impacts personal running identities
Bibliographic record
Abstract
Whereas running is inherently independent, it is often a social endeavor – conducted in groups and sustained by socially-derived motives. Based on this understanding, previous correlational research reveals that personal running identities are related to (but distinct from) runners' social identification with running groups (Strachan, Shields, Glassford, & Beatty, 2012). To better understand how group memberships impact personal running identities, the current online experimental study examined whether priming running group membership influenced self-reported running identity and intentions. One hundred and three running group members (Mage = 45.31; SD = 10.82; 63% female) were randomly assigned to read one of two vignettes that required them to imagine a training session where they were running (a) on their own, or (b) within their running group. Participants then reported running intentions and rated their personal running identity, before completing additional items (i.e., demographics, describing running group, and manipulation check). Whereas running intentions did not differ by condition, participants reported stronger personal running identities after imagining running with their group, p = .04. Follow-up analysis revealed that this result was moderated by sex, whereby the effect was primarily evident among female running group members. Although these findings are consistent with expectations for how group membership contributes to personal identities, they also support emerging results indicating that sex may shape how individuals connect to their groups (i.e., Gore, Bowman, Grosse, & Justice, 2015).
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".