MétaCan
Menu
Back to cohort
Record W4413834836 · doi:10.24908/iqurcp18991

The Reduction of Identity: How consent changes everything in street photography

2025· article· en· W4413834836 on OpenAlexaffvenue
Carolyn L. Kane

Bibliographic record

VenueInquiry Queen s Undergraduate Research Conference Proceedings · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicPublic Spaces through Art
Canadian institutionsQueen's University
Fundersnot available
KeywordsPhotographyIdentity (music)Reduction (mathematics)Internet privacyAestheticsSociologyVisual artsArtAdvertisingMedia studiesBusinessComputer science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.502
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0010.004
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.132
GPT teacher head0.429
Teacher spread0.297 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

Explore more

Same venueInquiry Queen s Undergraduate Research Conference ProceedingsSame topicPublic Spaces through ArtFrench-language works237,207