Legacy relative to opportunity: A novel framework for realising and assessing event hosting legacies of mega sporting events
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Event hosting legacies, the ability of host cities to capitalise upon mega-event hosting and utilise the acquired skills, knowledge and networks to secure an ongoing track record of event hosting, is a form of social legacy that has received limited dedicated study to date. The literature hints at the realisation of such legacies but this evidence base is patchy and the factors influencing legacy generation are largely unknown. This study seeks to address this gap by retrospectively applying a set of event hosting legacy indicators recently developed by Lockstone-Binney et al. (2023) to investigate the event hosting legacies of Olympic Summer Games and Olympic Winter Games Host Cities over the period 1988-2000. The cross-case analysis revealed that event hosting legacies were not homogenous across host cities with some having strong (Calgary, Seoul, Barcelona, Sydney), moderate (Albertville, Lillehammer, Atlanta) and limited (Nagano) evidence to support an event hosting legacy. The cross-case analysis revealed that a hosting legacy was influenced by structural and enabling contextual factors, which determine the extent to which legacy can be realised relative to opportunity.
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.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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 it