Understanding inclusion in collaborative governance: a mixed methods approach
Bibliographic record
Abstract
Abstract Who should be included in collaborative governance and how they should be included is an important topic, though the dynamics of inclusion are not yet well understood. We propose a conceptual model to shape the empirical analysis of what contributes to inclusion in collaborative processes. We propose that incentives, mutual interdependence and trust are important preconditions of inclusion, but that active inclusion management also matters a great deal. We also hypothesize that inclusion is strategic, with ‘selective activation’ of participants depending on functional and pragmatic choices. Drawing on cases from the Collaborative Governance Case Databank, we used a mixed method approach to analyse our model. We found support for the model, and particularly for the central importance of active inclusion management.
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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 | 0.133 | 0.142 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".