Bodying the War Correspondent: Deploying Creative Practice to Explore the Reporter's Body as a Sense-Making Tool
Bibliographic record
Abstract
In her 2021 essay, Bodying the Journalist, Chantal Francoeur acknowledges that journalists’ bodily experiences – “that complex assemblage of impulses, reactions, senses, emotions, energies and physical states” – are not easy to categorise or analyse. She argues that foregrounding the body both as an object of study and as a key resource for conducting research lies in part with journalists themselves investigating how they use their bodies as sense-making tools. This article operationalises Francoeur's three-dimensional framework for understanding how journalists deploy their bodies through the creation and analysis of a personal essay about the lived experience of a Reuters staff correspondent who was assigned to Afghanistan in 2011. The essay was written by one of the authors in journalism's creative non-fiction genre to explore her everyday encounters and embodied routines during a key period in the conflict that saw a surge in violence and coincided with the major news event of Osama Bin Laden's death. The personal essay's focus on how the journalist mobilised her body in her daily encounters with the new environment and interactions with others contributes to growing recognition of the emotional and physical work performed behind news headlines.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.017 | 0.055 |
| Scholarly communication | 0.019 | 0.010 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".