Care(lessness) in precarious journalism, before and during the pandemic: Freelancers’ work-life experiences and coping strategies
Bibliographic record
Abstract
Since the COVID-19 pandemic, interest in care as a potential remedy for a variety of issues and crises, such as improving global health justice or creating more “caring” educational systems, has increased across academic disciplines. This article contributes to this literature from the perspective of journalism studies. It explores whether the notion of care captures and addresses one facet of the contemporary news industry crisis, namely the precarity of journalists. I focus on the work-life narratives of a small group of freelance journalists in Germany and Canada before and during the COVID-19 pandemic. Applying a care lens articulates the struggles the freelancers experience regarding income, social security, housing, and childcare as a result of careless states and careless markets. Furthermore, the care practices journalists use as coping strategies merge mutual aid for fellow freelancers or members of their local communities with entrepreneurial networking for professional survival. However, such informal care practices cannot make up for structural gaps in support. Waged work—outside journalism—in the formal labor market, performed by the freelancers themselves or their life partners, turned out to be more important for coping with precarity. Overall, the pandemic meant more continuity than change for the care struggles as well as coping strategies of the journalists examined here. Nevertheless, those with residential property, gainfully employed life partners, and established care networks fared best, hinting at the crucial role of privilege in shaping the work experiences of precarious journalists during crises.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.027 | 0.026 |
| Scholarly communication | 0.016 | 0.007 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".