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Record W2786290840 · doi:10.1108/ccij-04-2017-0030

Vigilant resilience: the possibilities for renewal through preparedness

2018· article· en· W2786290840 on OpenAlexaboutno aff
Elizabeth Carlson

Bibliographic record

VenueCorporate Communications An International Journal · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicDisaster Management and Resilience
Canadian institutionsnot available
Fundersnot available
KeywordsPreparednessOriginalityResilience (materials science)Value (mathematics)SociologyProcess (computing)Public relationsPsychological resiliencePolitical scienceQualitative researchPsychologySocial psychologySocial scienceComputer scienceLaw

Abstract

fetched live from OpenAlex

Purpose Post-crisis renewal discourse (Ulmer et al. , 2007) is one form of communication that stakeholders may use as they attempt to organize for resilience. The purpose of this paper propose extending Discourse of Renewal Theory to explain how it could enact a different kind of resilience than scholars typically consider. Organizational resilience strategies often focus on the recovery or prevention stages of crisis management. Under conditions of persistent threat, it would be more productive for renewal discourse to emphasize greater preparedness. Design/methodology/approach To illustrate the need for this kind of theorizing, the author analyzes a case study that follows the public relations efforts of Canadian energy company Enbridge, Inc., in the aftermath of the 2010 Kalamazoo River oil spill. Findings By the criteria of Discourse of Renewal Theory, Enbridge attempted a renewal strategy, but it failed. By other criteria, however, it succeeded: it created the opportunity for richer dialogue among stakeholders about their interdependence and their competing interests. Originality/value By considering how elements of the resilience process may vary, this paper offers resources for more nuanced theory-building and theory-testing related to organizational and system-level resilience.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.547
Threshold uncertainty score0.999

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 studies0.0020.002
Scholarly communication0.0010.001
Open science0.0040.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.170
GPT teacher head0.434
Teacher spread0.264 · 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 designTheoretical or conceptual
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

Citations17
Published2018
Admission routes1
Has abstractyes

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Same venueCorporate Communications An International JournalSame topicDisaster Management and ResilienceFrench-language works237,207