Tracing the evolution of the gender of “COVID-19” in the French of three continents: A traditional and social media study
Bibliographic record
Abstract
Abstract In this article, we document the gender of the noun “COVID-19” in a database of more than 76,000 tweets and in traditional media (approximately 500,000 articles) in French as spoken in Africa, (North) America and Europe. We find that North American media comply near-categorically with the recommendations of the feminine by the World Health Organization and local linguistic authorities in March 2020. The majority of North American tweets follow suit soon after. The African data show an increase of articles and tweets adopting the feminine after the Académie française's recommendation in May 2020. Finally, the feminine is negligible in the European data. We argue that among the factors at play are dialect-specific differences in French gender and loanword adaptation; the complex relationship among linguistic authorities, the public, and local media; and the relative delay in the Académie française's recommendation of the feminine.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".