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Record W4234157206 · doi:10.1111/1467-9760.00091

Pornographies

2000· article· en· W4234157206 on OpenAlexaff
L. Green

Bibliographic record

VenueJournal of Political Philosophy · 2000
Typearticle
Languageen
FieldSocial Sciences
TopicAmerican Constitutional Law and Politics
Canadian institutionsYork University
Fundersnot available
KeywordsPornographyVirtueAutonomyPaternalismDemocracyLawSociologyCensorshipLegislationPoliticsMoralityPolitical science

Abstract

fetched live from OpenAlex

To be radical about pornography used to mean that one favored less censorship; now it often means that one favors more. That political change reflects a shift in the dominant paradigm of pornography and its putative evils. Until quite recently, most people who believed pornography wrong thought that it offended against decency and propriety and was therefore obscene . That was certainly the view of the law. English judges first created the crime of obscene libel in 1727 on the basis that such expression tended to corrupt the morals of the King's subjects, a thought that inspired most subsequent legislation in the common‐law world. Sometimes the underlying concern really was paternalistic: pornography degrades and corrupts its producers and consumers; the law forces them to become better people. More often, however, it was just moralism of the familiar sorts: the view that a majority of a community is entitled to enforce its moral views on the rest, either because that is democratic or because that is just what it means to be a community. The obscenity paradigm thus had two features. First, it was illiberal: it ranked personal autonomy below realizing the good, enforcing the majority will, or embodying communitarian values. Second, it was gender neutral: to understand the nature of pornography did not require theorizing relations between men and women. On the obscenity paradigm, pornography was a matter of virtue versus vice, majorities versus minorities.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.962
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.025
GPT teacher head0.313
Teacher spread0.288 · 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

Citations48
Published2000
Admission routes1
Has abstractyes

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