Picturing Haitian Earthquake Survivors: Graphic Reportage as an Ethical Strategy for Representing Vulnerable Sources
Bibliographic record
Abstract
This paper contributes to the scholarship on contemporary journalism practices in today's fast-changing media landscape, by focusing on graphic reportage, an emergent journalistic approach that relies on the medium of drawn comics. There has been much recent scholarship on this drawn form of journalism in the field of English literature. Yet there has been surprisingly little published on graphic reportage in the field of journalism studies. In order to address this gap, this paper presents some of the key the findings of a journalism practice-based research project that involved the making of an original work of graphic reportage based on fieldwork in a camp for Haitians displaced by the 2010 earthquake in Haiti. Reflecting critically on her own process of researching, writing, illustrating and designing this graphic project, the author shows that graphic reportage can help to explore the perspectives of people like displaced Haitians whose perspectives are often neglected in Western media. Discussing specific examples from this work of graphic reportage, the paper also demonstrates that this drawn form can be used to ethically visually represent such vulnerable sources in ways that would be more difficult if not impossible in more standard visual media forms.
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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 | 0.015 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.018 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| 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".