Worth a Thousand Words: Crime Scenes Represented by Photojournalists and Forensic Photographers in Brazil
Bibliographic record
Abstract
This article compares how crime scenes are represented by photojournalists and forensic photographers in the city of Brasília. It starts from the premise that crime scene photography adheres to the social and professional contexts of the professional group that is taking it who, in turn, determines its identities, practices and conventions. The methodology consists of an analysis of 168 photos taken of three cases of femicide that occurred between 2016 and 2019, and in-depth interviews conducted with three photojournalists and three forensic photographers. News photographs place their importance on the selection of information that is going to be shown to their audience, usually choosing people and elements that evoke emotions. Forensic photographers emphasize documentation and control, using high-angle shots to capture everything. Both fields claim objectivity in their production, but in opposite ways, as forensic photography attempts to prove what is shown, and news photography hides and distorts elements to appeal to its audience. This paper looks at the role photography plays in building social reality, showing the same scene from the same incident and how its representation may differ according to who makes the image, the protocols they follow, and also who these images are taken for.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".