Other- versus Self-Referenced Social Impacts of Events: Validating a New Scale
Bibliographic record
Abstract
Publicly funded sport events are partially justified based on positive social impacts. Past research generally measured social impact for a generic and global “other” with claims such as “Events create new friendships in the community”. These other-referenced (OR) social impacts are generally higher pre-event than post-event and are inflated for both methodological and theoretical reasons. In the pre-event period of the Tokyo 2020 Olympic and Paralympic Games, we empirically tested OR items compared to self-referenced (SR) items, such as “Because of the event, I create new friends in the community” and allowed projection bias to vary between scales. Results of the experiment between an OR-Social Impact Scale (OR-SIS) and a similar SR-SIS confirmed OR-measures to be significantly higher than SR-measures. While artificially inflated OR scores may be useful for event organizers and politicians to gain support for hosting, estimates based on circumscribed self (SR) are a methodologically appropriate measurement of social impact.
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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 | 0.029 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".