The common operating picture as a collaborative governance tool for urban resilience
Bibliographic record
Abstract
Abstract Through analysis of the narratives of participants to the London Common Operating Picture (COP), this study discusses the “information warehouse” and the “trading zone” theoretical perspectives on the COP that consider it, respectively, as an information system to share common information on a crisis or as a relational space of learning in which common meanings enable decision‐making and coordination. The trading zone approach has started to examine its role as a coordination tool, but there is little empirical research on how it can foster network's collaboration. Through an exploration of the COP participants' positions as boundary spanners, this study puts forward the trading zone perspective, showing that in London it has become a regular mechanism of collaboration and coordination. It argues that boundary spanners operate at multiple boundaries that enable network's collaboration and coordination, and the relational character of the COP create bonds of trust that foster collaboration and common understandings on the role of communication, collaboration, information sharing, and coordination for the security of all. We show that the COP is a relational space that creates a sense of belonging to a common community of security that believes in the role of networks' information sharing and collaboration for urban resilience.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.030 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".