Potential responses by on-campus university students to a university emergency alert
Bibliographic record
Abstract
University campuses across Canada and elsewhere are developing and implementing emergency alert systems to warn campus community members about a variety of threats. In this study, focus group discussions were used to examine how undergraduate students living on campus may respond to an emergency alert. A focus group activity used tornado, fire and threatening message alert messages to provide a context for the focus group discussions. After reading the warning message, most students understood the warning message but there was uncertainty about the non-specified threat and how and where to evacuate. Many would believe a message sent by the university as long as it was sent via a phone number that they associated with the university. Personalization of risk varied, and students reported that they would confirm a warning message with a variety of sources including student colleagues, faculty and teaching staff, television and internet sources. Taking protective action by sheltering in place was deemed to be feasible, however evacuation off campus was found to be problematic. We found that the nature of short message service text messages, the characteristics of universities, and the students’ home being in an on-campus residence influenced how the students may respond to an emergency alert message.
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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".