Examining the Evolution of Network Governance Forms of an Event Leveraging Collective: A Longitudinal Investigation
Bibliographic record
Abstract
This study aims to investigate how an event leveraging collective’s network governance forms evolved from pre- to post‐Games and how these forms influenced members' collaborative engagement. We adopted a longitudinal qualitative case study approach. Our primary data sources included 996 pages of archival documents and 18 interviews with collective members. We found that in the pre- and during-Games stage, the leveraging collective adopted a shared participant-governed form with a facilitator. This participant-driven structure could enhance connectedness and collaborative engagement among member organizations. In the postevent phase, the collective assumed a pillar-governed form with a facilitator. Although this structure appeared to promote within‐pillar collaboration, it could compromise cross-pillar integration. This study sheds light on the changing nature of an evolving leveraging collective from pre‐ to post‐Games. This study also provides practical implications for how to maintain a leveraging collective and optimize collaborative engagement among member entities in the long term.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".