Collaborative crisis management: a plausibility probe of core assumptions
Bibliographic record
Abstract
Abstract In this article, we utilize the Collaborative Governance Databank to empirically explore core theoretical assumptions about collaborative governance in the context of crisis management. By selecting a subset of cases involving episodes or situations characterized by the combination of urgency, threat, and uncertainty, we conduct a plausibility probe to garner insights into a number of central assumptions and dynamics fundamental to understanding collaborative crisis management. Although there is broad agreement among academics and practitioners that collaboration is essential for managing complex risks and events that no single actor can handle alone, in the literature, there are several unresolved claims and uncertainties regarding many critical aspects of collaborative crisis management. Assumptions investigated in the article relate to starting-points and triggers for collaboration, level of collaboration, goal-formulation, adaptation, involvement and role of non-state actors, and the prevalence and impact of political infighting. The results confirm that crises represent rapidly moving and dynamic events that raise the need for adaptation, adjustment, and innovation by diverse sets of participants. We also find examples of successful behaviours where actors managed, despite challenging conditions, to effectively contain conflict, formulate and achieve shared goals, adapt to rapidly changing situations and emergent structures, and innovate in response to unforeseen problems.
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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 | 0.065 | 0.242 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.006 | 0.031 |
| Scholarly communication | 0.010 | 0.024 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".