Issues and Strategies Pertaining to the Canadian Governments' Coordination Efforts in Relation to the 2010 Olympic Games
Bibliographic record
Abstract
The purpose of this article was to understand the government stakeholder group's coordination issues and strategies in mega-events, here, the 2010 Olympic Games. The case study was built by means of archival material, interviews, and observations. All three levels of government were included (i.e., the two host municipalities, the host province, and the federal government). Findings highlight five contextual-based issues (time, geography, funding, other resources, and the political situation) and eleven other types of issues (accountability/authority, activation/leveraging, knowledge management, legal, operational, planning, power, relationships, social issues, structure, and turnover). Eight strategies were used to address these issues: communication processes, decision-making frames, engagement, flexibility, formalized agreements, human resource management procedures/principles, strategic planning, and structural framework. The relationships between issues and from issues to strategies are discussed, as are within-group stakeholder heterogeneity and the impact the findings have on public administration theory and practice.
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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.024 | 0.009 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".