Applying event leveraging using OGI data: a case study of Vancouver 2010
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
Although the topic of strategically leveraging the hosting of sport mega-events to achieve socioeconomic outcomes has received some academic attention, research has not systematically applied the theoretical model of event leveraging. The challenge of this paper is to review some of the assumptions of the current theoretical model while using secondary data from the Olympic Games Impact (OGI) study that has been conducted on the Vancouver 2010 Winter Olympic and Paralympic Games. These data illustrate that, from the perspective of the host, the costs associated with leveraging far exceed the costs associated with hosting the Olympic Games. However, event leverage proves to be a complex process, in which it is nearly impossible to differentiate between impacts and outcomes, even when only government investments into infrastructure are illuminated. Therefore, a conceptual framework that links the sport mega-event with the context of the host is proposed and it is aimed at inviting and guiding future research by clearly assigning the responsibility of leveraging to host governments, with the involvement of other event stakeholders throughout the process.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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