Bibliographic record
Abstract
Why is "France Says COVID- Is Defi nitely Female" (Ledsom ) newsworthy? Does it matter if the term "COVID" is female? Linguistically, nouns and subjects vary in how they are gendered in most languages. L' Acadmie Franaise, the Offi ce Qubcois de la Langue Franaise (Quebec Board of the French Language), and the Royal Spanish Academy recommend treating "COVID" as feminine because the original English word-coronavirus disease-is translated to la maladie du coronavirus, which is feminine (Ledsom ). English linguists posit that complex concepts like COVID and government contribute to "a bigger argument about linguistic purism and the practice of gendering in language" (Tavarez ). Adverse events are typically masculine in Arabic and omnipotent events are feminine, so COVID- is masculine as a virus and feminine as a pandemic (Jaeha). One can argue that global responses did not measure up to the complexity of the pandemic regardless of the naming convention. However, COVID- has had a disastrous impact on families, economies, and communitiesespecially women.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.045 | 0.012 |
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".