Making Sense of Climate Change: The Challenges and Promises of Embodied Climate Journalism
Bibliographic record
Abstract
This article analyses examples of how journalists use their bodies and draw from their senses in climate reporting published in Canada between 2019 and 2024. Francoeur’s arguments for “bodying the journalist” are used in this paper as a conceptual framework to investigate the promises and challenges of embodied climate witnessing by journalists and define characteristics of journalists’ embodiment in storytelling (Francoeur, C. 2021. “Bodying the Journalist. (Reprint.).” Brazilian Journalism Research 17 (1): 202–227. https://doi.org/10.25200/BJR.v17n1.2021.1354). Through discourse analysis of 51 stories, key characteristics of embodiment in storytelling are identified, including discussing and interrogating first-hand experiences, gauging change or difference through the senses, and situating bodily experiences alongside scientific and future-oriented discourses. This article suggests that when journalists use their bodies to share how they feel when exposed to extreme conditions or events as a result of climate change, their stories can contribute to a shared public record of what climate change feels like as it happens. Embodied reporting can in turn make journalists co-subjects of their stories, testing journalistic norms related to objectivity and distance.
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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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.049 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".