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Record W2011811820 · doi:10.1386/macp.5.3.183_1

Writing the body: The hypertext of photography

2009· article· en· W2011811820 on OpenAlexaffabout
Kalli Paakspuu

Bibliographic record

VenueInternational Journal of Media and Cultural Politics · 2009
Typearticle
Languageen
FieldArts and Humanities
TopicPhotography and Visual Culture
Canadian institutionsYork University
Fundersnot available
KeywordsRhetoricNarrativeSubject (documents)PhotographyVisual artsIdentity (music)Cultural memorySociologyHypertextMnemonicIndigenousMedia studiesHistoryAestheticsArtLiteraturePsychologyLinguisticsAnthropologyComputer science

Abstract

fetched live from OpenAlex

In Canada and the United States the transformative value of a photograph was quickly recognized for nation building, and this new invention soon served a purpose in public memory. Its uses expanded from surveying lands to promoting population growth, tourism, artistic expression and to imagining virtual communities. Photography's narrative, however, offers readers a commentary on knowledge, identity and memory within an interactive space that is a dialogue between subject and photographer and a visual writing the body. A subject's presence and intentionality through this writing the body is a significant locus for knowledge and alterity - the construction of cultural otherness. Indigenous people viewing early photographs of family members may glimpse past a genre's rhetoric and actually experience their people in a lived historical moment. This essay examines the genre rhetoric of E.S. Curtis's photographs and Indigenous writing the body as a source of mnemonic knowledge. Photography addresses our senses and extends them while it gives form to a knowledge of being in a photographic hypertext that evokes public memory and references the personal, social and political.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.042
Threshold uncertainty score0.083

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0070.015
Scholarly communication0.0100.005
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0090.001

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 head0.029
GPT teacher head0.283
Teacher spread0.254 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations3
Published2009
Admission routes2
Has abstractyes

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