Ensuring volunteer impacts, legacy and leveraging is not “fake news”: Lessons from the 2015 FIFA Women’s World Cup
Bibliographic record
Abstract
© 2020, Emerald Publishing Limited. Purpose: This study aims to explore the legacy potential of the FIFA Women’s World Cup (FWWC) 2015, for the host communities across Canada. Design/methodology/approach: The mixed-methods study included a link to an online anonymous survey being sent to all volunteers at the FWWC that explored their prior volunteering experience, motivations for volunteering, perceived skill development and future volunteering intentions. Documents were reviewed, and key stakeholders were interviewed. Findings: The results support previous research that mega-sport event (MSE) volunteers are typically older females with prior volunteering experience. Those most likely to indicate they wanted to volunteer more are younger volunteers without prior volunteering experience. While legacy was discussed as a desired outcome, this was not operationalised through strategic human resource strategies such as being imbedded in the position descriptions for the volunteer managers. Research limitations/implications: As this study was conducted in the real-world context of a sport event, the timing of the survey was determined by the organising committee. Practical implications: Mega sport events typically draw upon existing host-city social and human capital. For future event organising committees planning for and delivering a volunteer legacy may require better strategic planning and leveraging relationships with existing host-city volunteer networks. In the context of a single sport, women’s MSE, multi-venue, multi-province event, greater connection was required to proactively connect younger women for volunteers to their geographic sport and event volunteering infrastructure. Originality/value: This is the first research of volunteers for the largest women’s mega single-sport event. There are three theoretical contributions of the paper to: the socio-ecological lens, motivational theory of single event MSE and the contribution of social and human capital to understandings of legacy.
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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 | 0.047 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.011 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| 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".