Mass email risk communication: Lessons learned from COVID-19-triggered campus-wide evictions in Canada and the United States
Bibliographic record
Abstract
From an out-of-province/state and international post-secondary student perspective, this article (a) explores mass email risk communication facilitation during the COVID-19-triggered campus-wide evictions in Canada and the United States; and (b) develops relative recommendations to improve mass email risk communication strategies for future emergency response. Investigating mass email risk communication-related impacts on students in a tertiary educational context has revealed a significant deficit in emergency response research, practice, and policymaking. Mandatory temporary university and college closures during the COVID-19 first wave provided an opportunity to address this research and practice deficit, as most Canadian and American universities/colleges administered their eviction communication via daily mass email chains. Through a phenomenological lens, this study interviewed twenty out-of-province/state and international students, ten from each country respectively, to examine student eviction experiences associated with intensive mass email risk communication. This research identified four factors linked to mass email risk communication: email chain characteristics, student interpretation, interdepartmental cooperation, and frontline voices. Synthesizing these findings, four evidence-based recommendations were developed: to efficiently convey risk information to students, to understand student perceptions and to inform their behaviors, to enhance interdepartmental cooperation, and to enable mutual dialogue in decision making. These recommendations could assist post-secondary institutions, and other organizations, in strengthening their mass email risk communication strategies and advancing organizational emergency response plans for future extreme events.
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.
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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.024 | 0.009 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.006 |
| 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".