New notions of soft power: Impact rhetoric in mega-event bid documents
Bibliographic record
Abstract
This viewpoint addresses notions of impact and soft power. Two bidding documents submitted in 2018 by Morocco and a joint bid by Canada, Mexico, and the United States are explored, focusing specifically on language used when discuss the term “impact.” Soft power is important to consider and use as a framework for interpretation because bidding for events involves the ability to persuade and use power as a medium to showcase the ability to host. Both bid proposals place less attention on economic impact, and emphasize the social and environmental impact that these events will have. Each bid document had a defined statement on legacy, but legacy did not dominate either bid as both put focus on how they would create impact in the present time. This approach is something that brings people into the directions of the bid, in terms of how social, economic, or environmental impact would be achieved, and directives positioned how they would make people aware of 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 | 0.020 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.010 | 0.043 |
| Scholarly communication | 0.023 | 0.034 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 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".