The impact of online trauma threats faced by journalists: The case of COVID-19-imposed remote-working regimes
Bibliographic record
Abstract
Abstract The global reach of the COVID-19 pandemic, with its sustained infection and fatality rates from the first quarter of 2020, has deeply affected the majority of journalists across the world, who now find themselves working on stories of trauma linked to the pandemic from remote locations and under restrictive working conditions. These COVID-19-enforced working conditions have exponentially increased the exposure levels of online trauma threats faced by journalists. This research examines the confluence of online trauma threats and their manifestations and impacts, along with mitigative measures some journalists took to ease the impact of this confluence. The research is guided by the central question: ‘How are journalists experiencing and responding to online trauma threats they face in the line of work during and ‘post’ COVID-19 lockdowns?’ The research utilises three distinct yet interrelated methods: an online survey; in-depth, semi-structured interviews; and narrative case studies in the form of feature-length journalism. Thematic analysis of the survey and interviews provides a framework for the works of journalism, which are situated in broader contexts of the journalism profession and online trauma reporting. Responding to the increase in online trauma threat activity exacerbated by the COVID-19 pandemic, the research points towards potential transformations within the profession that might assist journalists to continue undertaking their important role in and for society.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.029 | 0.018 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".