Assessing the use of explanatory journalistic texts for crisis communication education during the COVID-19 pandemic
Bibliographic record
Abstract
The ongoing COVID-19 pandemic brings both challenges and opportunities to crisis communication education. This article reports on student perceptions of an upper-level undergraduate course on risk and crisis communication that was taught in the fall 2020 semester. The course syllabus was redesigned to assist students in better understanding pandemic-related communication challenges. For this purpose, I adopted explanatory journalistic texts published by The Conversation Canada as supplementary readings for weekly in-class discussions. I subsequently conducted a survey near the semester end to explore how the course attendees perceived the revised syllabus. According to the survey results, the course attendees overwhelmingly welcomed a crisis communication syllabus built around an ongoing crisis. They also expressed a strong preference for the adoption of explanatory journalistic texts as supplementary readings. Overall, this classroom assessment demonstrates the advantages of teaching crisis communication theories and principles through the combination of explanatory journalistic texts and real-world cases, which other communication courses may adopt in the future.
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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 | 0.011 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".