Designing Collaborative Governance Decision-Making in Search of a ‘Collaborative Advantage’
Bibliographic record
Abstract
Collaborative governance institutions consisting of government and civil society actors often emerge to solve complex policy problems. Yet decades of research on collaborative governance has found that realizing the ‘collaborative advantage’ is often very difficult given the multitude of actors, organizations and interests to be managed. This article deploys a participant observation approach that also harnesses data from a natural experiment in collaborative governance for homelessness policy in Vancouver, Canada, to reveal the distinct collaborative advantage produced in terms of policy, using empirical decision data and counterfactual analysis. The data reveal that nearly 50 per cent of the policy decisions made in the collaborative institution would not be made in the alternative scenario of unilateral bureaucratic control. The collaborative advantage realized in this governance institution that is premised on horizontality, deliberation and diversity is the result of a series of small interventions and the strategic deployment of rules devised by the bureaucratic metagovernor in charge of steering the governance collaboration.
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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 | 0.053 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".