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Record W7132881155

Carnal Indexing

2017· article· en· W7132881155 on OpenAlexaff
Patrick Keilty

Bibliographic record

VenueTSpace · 2017
Typearticle
Languageen
FieldPsychology
TopicSexuality, Behavior, and Technology
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsAmateurSearch engine indexingMetadataRelation (database)Subject (documents)Schema (genetic algorithms)Object (grammar)Embodied cognitionProcess (computing)
DOInot available

Abstract

fetched live from OpenAlex

While online pornography’s unusual indexes may look disorderly, in fact, they evidence the process by which viewers and algorithms interact to arrange digital materials stored in databases of amateur pornography. These arrangements take shape according to patterns of browsing that serve as algorithmic data for the continuous process of organizing sexual representations. Porn sites and search engines offer a false impression of electronic metadata’s accessibility and expanse. Indexing requires discernible metadata in order to make database retrieval effective. Images are available to viewers through the negotiation of an elaborate schema in which categories of sexual desire are produced through the sequencing of fixed subject positions always defined in relation to each other. This essay will consider both sides of that organizational process. First, I will examine how the carnal aspects of browsing pornography online create a conjoined relation between subject and object in our embodied engagements with intermediating technology. Second, I will explain how this carnal activity informs this arrangement, through algorithms, of online pornographic images. Doing so reveals that pornographic video hosting services are not merely repositories for content. Instead, their visual and technical design highlights and privileges the conjoined and dynamic relations between body, machine, and representation.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Other
About the Canadian research system: no · About a Canadian topic: no
Not applicablelow
gptno category
Domain: not available · Genre: Other
About the Canadian research system: no · About a Canadian topic: no
Theoretical or conceptuallow
models splitAgreement compares identical category sets and study designs across arms.

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)0.015
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: Empirical · Consensus signal: none
Teacher disagreement score0.188
Threshold uncertainty score0.630

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.015
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0080.010
Science and technology studies0.0060.004
Scholarly communication0.0120.014
Open science0.0030.009
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.1880.069

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 head0.094
GPT teacher head0.452
Teacher spread0.359 · 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

Labeled directly by 2 models reading the full record.

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

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designNot applicable · Theoretical or conceptual
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

Citations0
Published2017
Admission routes1
Has abstractyes

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