Bibliographic record
Abstract
Science and truth during the covid-19 pandemic P G Brindley professor of critical care medicine, ethics, anesthesiology, consultant intensive care medicine During the covid-19 pandemic, I became one of those talking heads on the TV and radio in Canada hoping to reassure communities and recommend a way forward.While doing so, I found myself in parallel discussions about two issues: what truth is and is not and what science is and is not.I may not have always succeeded in resolving these debates, but among many covid lessons, I came to appreciate how science, truth, and the scientific method are often under attack.More specifically, I came to realise that, in any debate, "truth" can be the first victim and science can be readily weaponised.
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 | 0.029 | 0.113 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.017 | 0.011 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.045 | 0.044 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".