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Record W2791634077 · doi:10.1177/1206331217753344

Beyond Picturesque Decay: Detroit and the Photographic Sites of Confrontation Between Media and Residents

2018· article· en· W2791634077 on OpenAlexaff
Martin Zebracki, Brian Doucet, Toha De Brant

Bibliographic record

VenueSpace and Culture · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicParticipatory Visual Research Methods
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsPhotographyTransformative learningNarrativeSociologyScholarshipCitizen journalismAgency (philosophy)HegemonyCityscapeMedia studiesAbandonment (legal)NeglectSubject (documents)Everyday lifePhotovoiceNarrative inquiryLived experienceAestheticsVisual artsSocial scienceArtPsychologyEpistemologyPolitical scienceLiterature

Abstract

fetched live from OpenAlex

This article contributes to scholarship on urban visual culture by advancing understandings of how visual imageries may become (online and off-line) sites of confrontation between dominant media perspectives and the lived experiences of urban citizens. Based on participatory photography amongst local residents in Detroit, this article provides transformative insights by contrasting sense makings of Detroiters with dominant media portrayals of a “decaying” city. Residents were asked what images they would use to “see” and represent the city. Photo elicitation interviews revealed interlaced lived experienced and narrated reminiscences of local life and the material urban fabric beyond the prevailing narratives of mere neglect and abandonment. This study develops further knowledge of how photography can simultaneously operate as a critical socio-spatial research subject and an empowering tool for research participants. Through shifting the hegemonic locus of media agents toward residents’ positionalities, findings indicated potentials for redressing the misunderstood spaces of the everyday life of ordinary people.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.010
Scholarly communication0.0060.004
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.186
GPT teacher head0.520
Teacher spread0.334 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations18
Published2018
Admission routes1
Has abstractyes

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