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Record W7130911038

The rising tide of photographs: Not drowning but waving?

2016· article· en· W7130911038 on OpenAlexaboutno aff
Annebella; id_orcid 0000-0002-4896-8702 Pollen

Bibliographic record

VenueUniversity of Brighton Repository (University of Brighton) · 2016
Typearticle
Languageen
FieldArts and Humanities
TopicPhotography and Visual Culture
Canadian institutionsnot available
Fundersnot available
KeywordsSnapshot (computer storage)Subject (documents)Variety (cybernetics)PhotographyThe arts
DOInot available

Abstract

fetched live from OpenAlex

This article explores responses to photography's twenty-first century massification, as a key aspect of the 'condition' that curator Joan Fontcuberta (2015) has coined as 'post-photographic'. Through an assessment of a variety of current forms - popular press opinion, leading-edge arts practice and large-scale community projects - it offers a brief snapshot of the hopes and fears attached to photography en masse. By contextualising these responses within recent scholarly literature and also within historic instances of massification, this piece assesses and challenges the technologically determinist claims made for mass photography's novelty. Finally, it offers some methodological reflections and suggestions for ways to understand mass photographic practice, old and new. The article appears as part of the inaugural issue of a new bilingual interdisciplinary journal, Captures, on the subject of Post-Photography, edited by Canadian art historians Vincent Lavoie and Martha Langford.

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)0.004
metaresearch head score (Gemma)0.016
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: none
Teacher disagreement score0.037
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0130.026
Scholarly communication0.0090.009
Open science0.0010.006
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0080.001

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.

Opus teacher head0.012
GPT teacher head0.167
Teacher spread0.155 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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