Heeding the Warning Signs: Investigating Crisis Communications at Trent University in the Aftermath of Virginia Tech
Bibliographic record
Abstract
Using the case study model, the effects of an American tragedy on the communications planning and overall crisis mindset of a Canadian university are examined. The goal of this case study was to assess how Trent University in Peterborough, Ontario was impacted by the events that unfolded at Virginia Tech in Blacksburg, Virginia, where a lone gunman shot and killed 32 students and staff before taking his own life. Four administrative leaders at Trent were interviewed and existing crisis planning documents were analyzed. The results revealed that a parallel crisis can have a substantial impact on an organization’s crisis mindset in the months immediately following the event. The effect of Virginia Tech on Trent resulted in: new lessons learned by senior administrative staff; a heightened awareness on the campus of risk factors; and enhancement of the university's crisis plan.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.007 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| 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".