READINESS as a new framework for crisis management: academic-industry integrated expert insights from practitioners and scholars
Bibliographic record
Abstract
Purpose The study provides an initial empirical examination of Jin et al .’s (2024) new READINESS model through the expert opinions of crisis communication academics and practitioners. Through this examination, the goal is to understand crisis READINESS and how it relates to other key concepts in the crisis literature, such as preparedness and resilience. Design/methodology/approach An exploratory quantitative online survey of 30 experts in crisis communication was conducted. Our participant pool consisted of members from the Crisis Communication Think Tank, which is an established crisis thought leadership network (Jin, 2023). Data collection took place in November and December 2023. Findings Key findings include the dual nature of crisis READINESS as both a process and an outcome, resilience as both a process and an outcome, and preparedness as an antecedent to READINESS. A key distinction between READINESS and preparedness emerged with the former conceived of as a mindset and the latter conceived of as physical tools, training and planning. Originality/value Preparedness and resilience alone are not enough to effectively manage crises and risks, and given this, it is important to study READINESS as a concept beyond (yet connected to) preparedness and resilience. It is our hope that the findings can lead to understanding indicators of crisis READINESS and developing crisis READINESS measurement tools which can equip organizations to more effectively manage crises.
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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 | 0.045 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.006 | 0.020 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".