Is Covid-19 “vaccine uptake” in postsecondary education a “problem”? A critical policy inquiry
Bibliographic record
Abstract
Since the launch of the Covid-19 global vaccination campaign, postsecondary institutions have strongly promoted vaccination, often through mandates, and the academic literature has identified "vaccine uptake" among postsecondary students as a problem deserving monitoring, research, and intervention. However, with the admission that vaccines do not stop viral spread, that older-age and co-morbidities are major determinants of poor outcomes, and that many vaccine side effects disproportionately affect the young, it cannot be assumed that a risk-benefit analysis favors vaccinating postsecondary students. Drawing from critical policy studies, I appraise the literature on Covid-19 vaccine uptake in postsecondary education. I find that this literature reflects the "scientific consensus," hardly acknowledging contradictory medical evidence, ignoring coercive elements underlying "vaccine acceptance," and neglecting ethical tensions built into the very design of vaccination policies. I discuss potential explanations for my findings, and their implications for academia's role in society in the COVID-19 era and beyond.
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 | 0.104 | 0.234 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.013 | 0.048 |
| Scholarly communication | 0.021 | 0.017 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.019 | 0.018 |
| 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".