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Record W238612893 · doi:10.15173/mjc.v6i0.245

Heeding the Warning Signs: Investigating Crisis Communications at Trent University in the Aftermath of Virginia Tech

2010· article· en· W238612893 on OpenAlex
Brittany Cadence

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueThe McMaster Journal of Communication · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicPublic Relations and Crisis Communication
Canadian institutionsMcMaster University
Fundersnot available
KeywordsVirginia techWarning signsCrisis communicationCrisis responseWarning systemPolitical scienceEngineeringTelecommunicationsLibrary sciencePublic relationsComputer scienceTransport engineering

Abstract

fetched live from OpenAlex

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.

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.

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)0.008
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Open science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.390
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0000.001
Open science0.0060.001
Research integrity0.0000.001
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 head0.039
GPT teacher head0.312
Teacher spread0.274 · 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