Impact of COVID-19 on Working Arrangements and Working Experiences at Western University
Bibliographic record
Abstract
The data presented in this report was collected as part of the COVID Homeworking for University Staff Survey (CHUSS) project. CHUSS is an international research collaboration looking into the impact of the COVID-19 pandemic on working arrangements at universities. It is lead by Dr. David Peetz at Griffith University, Australia and involves researchers at 14 universities – seven in Australia and seven in Canada. An online survey was sent to academic staff (i.e., faculty, librarians and archivists), non-academic staff in administrative, professional and general roles and senior administration at each participating institution between July and October 2020. A total of 12,844 employees participated.This report focuses on the survey data collected among academic and administrative/general staff at Western University between July 9 and August 10, 2020. The survey was distributed via the centralized email system to 7246 people employed by Western as of the survey date, excluding students. In total 1239 people responded to the survey for a response rate of 17%.
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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".