Bibliographic record
Abstract
While the fight against the COVID-19 pandemic remains centered around the great strides made in fields of biotechnology and epidemiology, an epidemic of misinformation and growing skepticism of the scientific community rages beneath the surface. Almost understandably so, in an unprecedented time of change riddled with fear and a sense of loss, people turn to thoughts, emotions, and behaviours that sometimes do more to impede the return to normalcy that we all crave. Reports that were particularly prevalent during the early pandemic response, including those of individuals refusing to wear masks in public spaces or anti-lockdown rallies throughout North America, only spurred further confusion and divisive sentiments on both sides [1]. While these events may point towards a lack of clear communication and mixed messaging from authority figures in the early response, a culture of inherent skepticism, particularly on social media, continues to be pervasive. With hopes of a global re-opening riding on the current vaccine rollout, widespread acceptance of the COVID-19 vaccines remains essential to achieving herd immunity and ultimately curbing the spread of the virus. However, marginalized and underrepresented groups in North America that have been most heavily affected by the pandemic are also often those who are most distrustful of the medical system [2,3].
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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.021 | 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".