Bibliographic record
Abstract
In March 2020, even after the World Health Organization (WHO) declared COVID-19 a pandemic, Labrador Diagnostics attempted through “the most tone-deaf IP suit in history” 1 to block testing for coronavirus that used its patents. In June 2020, Gilead Sciences shocked the international community by pricing its patented medicine remdesivir at $3,120 per course of treatment for COVID-19 patients with private insurance in the United States. Thereafter, the company was vehemently accused of overcharging in “an offensive display of hubris and disregard for the public,” 2 which also led to serious concerns that global efforts to contain the pandemic were effectively at the mercy of medical patent owners. 3 Since December 2020 when the first COVID-19 vaccine was approved by the US Food and Drug Administration, vaccine inequity has ravaged the globe. As of September 2021, a mere 3 percent of people in low-income countries had been vaccinated with at least one dose, in stark contrast to high-income countries’ 60 percent.
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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.274 | 0.219 |
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".