Final Reflections: Correcting Misrepresentations and Charting Future Directions in Crisis Communication Research Through an Evidence-Based Lens
Bibliographic record
Abstract
This article addresses significant misrepresentations of my vision for the future of Crisis Communication Research (CCR) and reinforces what I see as useful directions for CCR. My proposed future directions are not limited to corporate crises or solely experimental methods. The concepts such as READINESS, affective polarization, crisis victims, and sticky crises are applicable across public, political, and corporate subfields. I advocate for an evidence-based (EB) approach to CCR, which values insights from all research methods, including experimental ones, and for the value in establishing cause-effect relationships that inform interventions.
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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 | 0.261 | 0.451 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.012 | 0.056 |
| Scholarly communication | 0.035 | 0.043 |
| Open science | 0.009 | 0.021 |
| Research integrity | 0.021 | 0.061 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".