Reactions of Facebook Users to Ontario University Mask and Vaccine Mandates
Bibliographic record
Abstract
During the height of the COVID-19 pandemic, educational institutions worldwide experienced significant disruptions to in-person learning. Following a period of online learning, Canadian universities initiated a cautious return to campus, accompanied by new rules and regulations intended to keep campus communities safe. Common among many institutions was the implementation of mask and vaccine mandates, which generated significant discussion on social media platforms. Such strong responses to these regulations create an opportunity for academic investigation, as researchers can use this real-life experience to discern whether the public experiences emergency safety mandates as beneficial or disruptive to their lives. This paper takes the form of a content analysis of comments from a prominent Ontario university’s official Facebook posts. It seeks to investigate the primary response of social media users to the implementation of mandates and whether sentiments remain constant among users of different relations to the university. Significant findings include the overwhelming presence of negative opinions towards the mandates and the lack of comments from current students of the institution under study. The analysis also revealed that users opposed to the mandates are likelier to post detailed comments backed up with outsourced information or strong emotional language. In contrast, positive posts were overwhelmingly short and lacked evidence of actionable intention to defend their viewpoint. These findings suggest that while those contributing positive comments may do so to signal their support or as a means of social interaction, social media users posting negative comments are more actively seeking change through their online interaction with the institution.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".