Bibliographic record
Abstract
The article examines the documentary work of British–Mexican filmmaker Trisha Ziff. It focuses on three films: Chevolution (2008), an examination of the iconic image of Che Guevara by Cuban photographer Korda; La maleta Mexicana/The Mexican Suitcase (2012), about a trove of negatives taken by war photographers Robert Capa, Gerda Taro and David Seymour during the Spanish Civil War and found in Mexico City decades later and El hombre que vio demasiado/The Man Who Saw Too Much (2016), a documentary that follows the life and work of Mexican tabloid photographer Enrique Metinides. The article show how Ziff examines issues around the moral and legal implications in the way images are disseminated and used, the power of photography to evoke and shape historical memory, and the shock value of images of violence. It argues that the three documentaries are valuable contributions to an archaeology of modernity’s visual media, looking back at a recent period where illustrated journalism was the foremost medium of information. Taking a cue from Cornell Capa’s notion of the ‘concerned photographer’, the article shows how, underpinning Ziff’s documentaries, the notion of the ethical mission of the photographer in an age increasingly dominated by the moving image, information overload and historical amnesia is paramount.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".