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Record W3103585884 · doi:10.1108/md-07-2020-0880

Too late to act: when crises become tragic

2020· article· en· W3103585884 on OpenAlexaff
Mary-Liéta Clément, Christophe Roux‐Dufort

Bibliographic record

VenueManagement Decision · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicPublic Relations and Crisis Communication
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsTragedy (event)MetaphorValue (mathematics)OriginalitySet (abstract data type)Greek tragedyOrder (exchange)Control (management)Field (mathematics)Law and economicsFinancial crisisEconomicsSociologyEpistemologyPositive economicsPolitical scienceLawManagementComputer scienceHistoryPhilosophySocial scienceKeynesian economicsFinance

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.807
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.068
GPT teacher head0.349
Teacher spread0.280 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2020
Admission routes1
Has abstractyes

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