In context : an examination of Larry Towell's work No man's land from the twin perspectives of maker and user
Bibliographic record
Abstract
Photojournalist Larry Towell is the only Canadian member of the prestigious Magnum Photos Agency. Over the span of his career he has concerned himself primarily with issues of land and landlessness and has engaged in a number of long-term projects documenting the human stories amid political and religious conflict in Central America and the Middle East; he has also chronicled the migrant Mennonite workers of Mexico. This thesis focuses specifically on Towell's work photographing the Israeli-Palestinian conflict in No Man's Land, a book project that was accompanied by newspaper and magazine publications, exhibitions, a video, audio CDs, public performances and multi-media projects. The extensive dissemination and documentation of Towell's work from No Man's Land offers an opportunity to examine a cohesive body of work in a number of forums and see how the context in which photojournalistic images appear can subsequently affect their meaning. This thesis undertakes a thorough examination of the photographs from No Man's Land in the context of the book, the printed press and exhibitions, considering the intent of the photographer in relation to audience perception of the work. With the new direction in photojournalism leading toward more subjective and self-reflexive projects and with expanding opportunities in digital and art worlds, it is essential that context and presentation be thoroughly understood to ensure the integrity of the issues and the photographer's intent.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.031 | 0.022 |
| Scholarly communication | 0.016 | 0.006 |
| Open science | 0.002 | 0.008 |
| 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".