Empowered Stakeholders: Female University Students’ Leadership During the COVID-19-Triggered On-campus Evictions in Canada and the United States
Bibliographic record
Abstract
Abstract The study of disaster-specific leadership of female university students has been largely neglected, especially during on-campus emergency eviction and evacuation. Based on the COVID-19-triggered, on-campus evictions across Canada and the United States, this cross-national partnership examined the out-of-province/state and international female university students’ leadership during the entire eviction process. Through in-depth interviews, this study revealed the female university students’ leadership behaviors during three stages: (1) pre-eviction: their self-preparedness formed an emotional foundation to support others; (2) peri-eviction: their attitude and leadership behavior enabled them to facilitate (psychologically and physically) their peers’ eviction process; and (3) post-eviction: they continued to support their peers virtually and raised the general public’s awareness regarding the plight of vulnerable and marginalized populations. This article argues that the female university students’ leadership that emerged during the eviction process became complementary to and even augmented the universities’ official efforts and beyond. This leadership represents empirical evidence that contributes to the existing literature on gender and leadership by demonstrating female youth as empowered stakeholders rather than as merely passive victims. Future studies could develop detailed stratification of gender and age dimensions in order to portray a more comprehensive picture of the younger generation’s leadership in hazards and disaster research and practice.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".