The Reduction of Identity: How consent changes everything in street photography
Bibliographic record
Abstract
Street photography as a genre is known for being quick, capturing a brief moment in public. Bruce Gilden (b. 1945) is a famous American street photographer based in New York, and known for his up-close flash photos, taken without the consent of the subject. Vivian Maier (1926-2009) is a mysterious figure, none of her street photographs were published during her lifetime which leaves many questions about her intent. Although Gilden and Maier are very different artists separated by time, both provide insight into the lack of control of a subject in their chosen genre. While the photos taken by Gilden and Maier depict the physical likeness of a person, I will argue that they are not portraits aimed at capturing a person’s likeness and identity. The distinction between street photography and portraiture lies in the subject’s ability to curate or exert control over how they are portrayed. By examining this disparity, I will show that when consent is not given, the resulting reduction becomes a potent manifestation of powerlessness, as the subject’s identity is distilled into an image that may fail to accurately reflect their sense of self; thus, the photograph separates the identity of a person from their physical likeness. This presentation is an exploration of how photography can neglect identity to focus on a particular moment in time.
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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 | 0.026 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.040 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".