2008 North American Society for Sport Management Conference (NASSM 2008)
Bibliographic record
Abstract
The 2010 Olympic and Paralympic Winter Games are becoming a turning point in both the Canadian sport system (e.g., increased focus on amateur (winter) sports, increased cooperation between actors in the system) and the Olympic Movement (e.g., presentation of the Games ' Multi Party Agreement, MPA, signed in the Bid phase, as a good tool for focusing and coordinating government involvement in the Games by the International Olympic Committee). As well, there is a growing sense of needing to empirically validate and quantify the commonly attributed benefits of hosting major sporting events; for example, do they increase sport participation at the grass-roots level; what is the extent of the economic impact across the country; can they effectively foster knowledge creation and transfer mechanisms, as well as cooperation, among key stakeholders? In Canada-and arguably in the rest of the world-there is a growing movement for accountability related to the public funding of mega events like the Olympic Games(i.e. evidence-based policy development). But how is this performance and accountability determined, measured, used? We will present an emerging partnership between academia and the Canadian federal government in relation to measuring performance and examining accountability. First, we will argue that a solid understanding of accountability in relation to the 2010 Winter Games must include topics such as 1) evaluating the extent of planning of performance and accountability mechanisms and compare with the actual outcomes (e.g., definitions, processes, complementarity of mechanisms, impact of bureaucracy, and transparency considerations); 2) determining who is accountable to whom and for what; 3) determining the perspective of the other stakeholder groups related to the
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.342 | 0.208 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".