Ensuring volunteer impacts, legacy and leveraging is not “fake news”
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".