Bibliographic record
Abstract
The COVID-19 pandemic showed the close relationship between the Canadian government and the pharmaceutical industry when it came to both domestic and international issues. Domestically, the government chose to prioritize advice about vaccine acquisition from a panel of heavily conflicted people; it signed contracts worth billions of dollars with companies for vaccines but the contents of contracts were largely kept secret. The government also committed over CAD$1 billion in funding for research on COVID-19 but without any requirement that any forthcoming intellectual property or diagnostic and therapeutic products had to be accessible and affordable in low- and middle-income countries (LMICs). On the international stage, Canada did not support the COVID-19 Technology Access Pool that aimed to provide a one-stop shop for scientific knowledge, data, and intellectual property to be shared equitably by the global community. It delayed donating vaccines to LMICs and bought vaccines from a facility designed mainly to provide vaccines to that group of countries. The government did not dismantle roadblocks that prevented a Canadian company from sending vaccines to Bolivia. Finally, it was ambiguous about whether it supported a patent waiver for COVID-19 technologies at the World Trade Organization.
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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.014 | 0.012 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.011 | 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".