“A Double-Edged Sword:” Revealing the COVID-19 Pandemic’s Disproportionate Impacts on the Productivity of Women Print Journalists through Mixed-Methods Research
Bibliographic record
Abstract
In 2020, surveys revealed the COVID-19 pandemic was increasing gender inequalities among different professions, including journalists and academics. There was therefore a need to examine the first wave’s impact on Canadian reporters. The aim of this study was to determine whether women and precariously employed journalists were unequally affected and, if so, to help prevent negative effects on them in the event of a future crisis. This mixed methods project used an explanatory sequential design. Quantitative data on productivity was measured by comparing the number of by-lines published by journalists in three daily francophone publications between March 1 and May 31, 2020, to the same period in 2019. Six semi-directed qualitative interviews with journalists picked from the quantitative sample were then conducted. Analyzed through thematic analysis, they explored the hypotheses formulated to explain the changes in productivity discovered and served to contextualize the quantitative results. Overall, journalists in the sample were slightly more productive than the previous year. The increase was driven by women, whose productivity increased by 7% (men’s decreased by 3%), and by staff, whose publication rate increased by 9% (precarious journalists’ decreased by 33%). Women staff’s productivity increased more than men’s while that of women precarious journalists decreased more. Based on these findings, we argue that women were disproportionately affected by the pandemic. Staff women likely experienced more pressure to produce because of the gendered division of beats, and therefore published more, while precariously employed women lost more work than men and likely experienced more financial insecurity.
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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 | 0.063 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".