Forced change: Talking trauma — how journalism educators are finding new ways to teach an age-old topic
Bibliographic record
Abstract
What does a “trauma-informed journalist” look like? What are the specific competencies associated with being a trauma-informed journalist? And what are the metrics used to measure the efficacy of current training on trauma-informed approaches to reporting? These questions grow out of a discussion at the Taking Care Roundtable, which brought newsroom leaders, journalism educators, working journalists, union representatives, and other stakeholders together in Ottawa in October 2022 for a two-day meeting intended to surface practical and innovative solutions to address some of the challenges highlighted in Matthew Pearson’s and David Seglins’s (2022) Taking Care: A report on mental health, well-being and trauma among Canadian media workers. This podcast episode features a discussion among journalism educators about the importance of teaching trauma-informed approaches to reporting, the current gaps in pedagogy and practice, and reflections on the student-led demand for this content in a post-pandemic environment where mental health and well-being is top of mind among many young journalists in training.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".