Good story, bad news: journalistic capital and occupational injury
Bibliographic record
Abstract
This narrative inquiry explores Canadian journalists’ perspectives and experiences around uncensored User-Generated Content (UGC) and trauma reporting from the digital frontline. Rich experiential data were generated through a series of one-on-one virtual interviews with four Canadian journalists. These discussions focused on topics from everyday emotionally demanding assignments involving uncensored UGC to trauma informed education, training, and practice in the field. The main themes identified were journalistic capital, the ubiquitous nature of trauma in daily news coverage, the structure of work, uncensored UGC, and lack of formal education, training, and supports. This thesis argues that UGC is changing how journalists source news material and interact with the public. With this shift comes new psychosocial hazards that must be addressed by journalism educators, newsroom managers, and occupational health and safety scholars and professionals. Bourdieusian thought was applied to the occupational health and safety of journalism and fills a knowledge gap in the occupational health literature as psychological injury is often studied in war correspondents and less so in relation to journalists exposed to psychosocial hazards while in pursuit of journalistic capital on the digital frontline.
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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 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.029 | 0.028 |
| Scholarly communication | 0.022 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".