Iterative Factors Favoring Collaboration for Interorganizational Resilience: The Case of the Greater Montréal Transportation Infrastructure
Bibliographic record
Abstract
Between members of a network, interorganizational resilience is favored by effective collaboration and coordination during a crisis. The quality of that collaboration depends on various iterative factors present between these organizations before the occurrence of a crisis. We find that these factors are iterative since collaboration factors follow a mutually reinforcing cycle: collaboration within a crisis management network is conditioned by a general agreement, which is in turn conditioned by the extent to which the institutions coordinate themselves prior to crisis. We evaluated the factors that promote collaboration between public and private organizations that manage the Greater Montréal transportation infrastructure. These factors are based on adaptive management processes such as mutual agreements, common organizational culture, knowledge and financial resources, levers of power, regulations, and pressure. Crisis management coordination represents the ability to build and assess the effectiveness of common response plans to risks to which they are exposed. We show how these processes vary depending on the links between private and public organizations.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".