Bibliographic record
Abstract
On March 11th 2020, the World Health Organization declared COVID-19 a global pandemic [1,2]. We had realized the gravity of the situation before but, until that day, did not comprehend how our reality was about to change for the foreseeable future. Following that declaration, most countries and states implemented strict public health restrictions to contain the spread of the virus and decrease its mortality rate by enforcing physical distancing measures. Albeit effective, such measures did not come without consequences on the quality of life and wellbeing of people worldwide [3]. Social isolation, loneliness, loss of employment and income, and housing instability were some of the adverse events that arose during what we call “the lockdown” [4,5]. A friend of mine who had migrated to Canada from a war-torn zone felt the magnitude of this lockdown. “It’s like moving from one prison to another” he explained, reminiscing about a time he was forced to stay home to avoid the unforgiving jaws of man-made conflict.
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 | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.012 |
| Scholarly communication | 0.017 | 0.024 |
| Open science | 0.005 | 0.021 |
| Research integrity | 0.011 | 0.034 |
| Insufficient payload (model declined to judge) | 0.032 | 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".