Developing Local Citizenship through Sporting Events: Balancing Community Involvement and Tourism Development
Bibliographic record
Abstract
Cities throughout the world have struggled to remain competitive in an era of globalisation and devolution. As a result, many have turned to tourism-related activities, such as hosting sporting events or mega-events, as part of development strategies (Hall, 1992). Within this context, questions of how these short-lived events affect resident and nonresident identities have been raised. In essence, questions of citizenship, community, and identity have become central with the on-going use of itinerant tourism strategies. Lepofsky and Fraser (2003) reasoned that community citizenship can no longer be viewed as a static concept, where rights to local citizenship are guaranteed by virtue of residential status. They propose the notion of flexible citizenship, where residents and nonresidents alike determine their level of citizenship by their ability to negotiate their contributions within the community. This paper uses this conceptualisation of citizenship to explore how community involvement in the hosting of sporting events – by organising, watching, or participating in an event – affects notions of community citizenship, and how these newly articulated citizenships affect tourism development.
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 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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.016 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".