Champ or chump: strategic utilization of dual social identities of others
Bibliographic record
Abstract
Abstract The Jamaican‐born, Canadian sprinter, Ben Johnson, won the gold medal at the 1988 Olympics, but afterward was disqualified for steroid use. At the time Johnson's identity in the Canadian media appeared to shift—he was ‘Canadian’ after winning the gold medal but ‘Jamaican’ after disqualification. We tested this hypothesis via an archival study of a newspaper database of Canadian newspapers. The results confirmed the speculation. In the second study with Canadian research participants, the nationality of a fictional athlete was experimentally manipulated. He either possessed Canadian‐ American (shared) or Belgian‐American (non‐shared) identity. The athlete's performance outcome at an Olympic event was also manipulated. In the shared identity condition the athlete was perceived as more Canadian when he won than when he lost. There were no significant differences in nationality judgment when neither of the athlete's dual nationalities was Canadian. Results regarding perceptions of similarity paralleled the nationality‐ratings results. Findings from these two studies illustrate an interesting extension of BIRGing and CORFing strategies in which multiple social identities of others are used strategically to include or exclude others from the in‐group. Copyright © 2007 John Wiley & Sons, Ltd.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".