Too late to act: when crises become tragic
Bibliographic record
Abstract
Purpose This article aims to explore the tragic nature of crisis and identify managers’ decision-making processes and strategies when they are trapped by events beyond their understanding and control. In this article, the tragic is viewed as the collision of an overdetermined scenario perceived as inevitable, insurmountable and irreparable and the managers' strategies to free themselves from this scenario and divert its trajectory. Design/methodology/approach We make a crossed literature review between crisis management and Greek tragedy as proposed by scholars in classical literature. Findings We make two theoretical contributions to the literature on crisis management. First, we articulate a set of research proposals into a model to explain how managers' decisions make the crisis tragic. Second, we enrich the field of crisis management by highlighting strategies in order to avoid them. Originality/value We use Greek tragedy, not as a metaphor to characterize the consequences of crises as the authors usually do, but as an analytical lens to explore their inexorable, insurmountable and irremediable nature and the decisions made by managers that would make crises tragic.
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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 | 0.005 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".