Bibliographic record
Abstract
As a result of the unexpectedly quick development of vaccines to prevent COVID-19, the Canadian government was pulled in two opposite directions. On the one hand, Canadians exerted extreme pressure on the government to purchase and roll out vaccines as fast as possible for domestic immunization. On the other hand, it sought to promote global access to the vaccine, which would save more lives. This article examines how the Canadian government responded to this quandary, why it made those choices, to what effect and what a better approach would have been. I argue that, by adopting a resolute “Canada First” approach for electoral reasons, while also rhetorically espousing equitable global access, the government tried to satisfy both sides. However, by focusing overwhelmingly “doing good” for Canadians, the government is also indirectly “doing harm” to vulnerable people abroad and prolonging the pandemic globally and for Canadians too. Canadian “vaccine nationalism” is also harmful to Canadian economic interests and claims of global leadership, and will reduce Canada’s “soft power”. The solution, from both an ethical and a pragmatic standpoint, would be to share vaccines more equitably and support intellectual property waivers and other measures to accelerate global vaccine production and immunization.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.084 | 0.012 |
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".