Empirical Issues and Challenges for Multilevel Governance: The Case of the 2010 Vancouver Olympic Winter Games
Bibliographic record
Abstract
How did a large network of over 600 actors successfully organize itself to serve a mega project dominated by three levels of government, even as control rested with a non-profit entity, included other sectors, and the governments involved did not normally work well together? The purpose of this paper is to examine how the three levels of government in Canada established a network to coordinate efforts for hosting the 2010 Vancouver Olympic Winter Games. This case study was built by means of documents and interviews, and supported by participant observations. The network was not found to be dense, but did include a multiplexity of ties (e.g., transactions, communications, collaborations, and coordinating bridges) by actors serving diverse strategic goals and scopes of work. The case was compared to data collected for the 2012 London Olympic Games to draw out key network governance coordination themes. Nine governance themes emerged associated with governance structure, processes, and evaluation: coordination mechanisms; internal engagement, momentum, and motivation; external transparency; formalization; balancing autonomy and interdependence; co-location; readiness exercises; political alignment; and time. The findings provide a framework for examining the governance of multi-level, multi-sectorial networks created to undertake a mega project and indicate how a network’s public and non-profit organizations’ activities and procedures can be influenced, modified, and impacted by the other actors (i.e., other public or non-profit organizations).
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.017 | 0.018 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".