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Record W3091838973 · doi:10.5210/spir.v2020i0.11278

BAD ATTACHMENTS: EMAIL AND QUEER ANTI-CENSORSHIP PROTESTS

2020· article· en· W3091838973 on OpenAlexaff
Cait McKinney

Bibliographic record

VenueAoIR Selected Papers of Internet Research · 2020
Typearticle
Languageen
FieldPsychology
TopicSexuality, Behavior, and Technology
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsCensorshipQueerInternet privacyThe InternetHuman sexualityCriminalizationSociologyPoliticsMedia studiesAdvertisingWorld Wide WebLawPolitical scienceGender studiesComputer scienceCriminologyBusiness

Abstract

fetched live from OpenAlex

This paper examines a 1996 U.S. internet censorship protest that encouraged users to email a series of technically “indecent” files as attachments to Speaker of the House Newt Gingrich using an online email generator. These attachments were: a list of abortion clinics, a graphic illustration of condom-use instructions, and excerpted sexually explicit scenes from Gingrich’s own novel, 1945. Selecting from a drop-down menu, senders chose their attachments, completed a personalized message, and clicked send, all within a web-based form. By using the platform to inundate the Speaker’s email with attachments, senders cleverly broke the censorship provisions of the 1996 Communications Decency Act (CDA), putting themselves at risk to the criminalization of sexual expression online. The “bad attachments” protest grew out of the fact that online information about sexuality was vital to marginalized communities with limited access to other kinds of information channels—including queer and rural youth, and people living with HIV. This paper argues that the protest attachments constitute a queer, material digital practice, attuned to the political demand for ready information access as a means of survival.

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)0.001
metaresearch head score (Gemma)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.076
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.094
GPT teacher head0.419
Teacher spread0.325 · 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 designObservational
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
Published2020
Admission routes1
Has abstractyes

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