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Unauthorized Comic Book Scanners

2014· book-chapter· en· W2495040730 on OpenAlexaff
Darren Wershler, Kalervo A. Sinervo, Shannon Tien

Bibliographic record

VenueAdvances in social networking and online communities book series · 2014
Typebook-chapter
Languageen
FieldBusiness, Management and Accounting
TopicCopyright and Intellectual Property
Canadian institutionsConcordia University
Fundersnot available
KeywordsComicsEphemeral keyMateriality (auditing)Internet privacyIdentification (biology)Computer scienceMedia studiesSociologyComputer securityArtAestheticsArtificial intelligence

Abstract

fetched live from OpenAlex

This chapter uses theories of circulation, subculture, and materiality to discuss the activities of unauthorized comic book scanners or “pirates,” and the mechanisms by which they structure their community. The discussion is drawn from a body of quantitative data collected by observing the circulation of unauthorized comic scans through several BitTorrent Websites between 2005 and 2012. The authors also examine the public discourse of scanners themselves—showcased through various anonymous interviews—as part of an investigation into the scanners’ identification with a system of ethics that validates their dissemination of unauthorized content in the name of preservation or “digital archiving.” Lastly, the authors propose a methodology for the study of digital media as “space-biased” and circulatory rather than archival. Though comic book scanners may identify themselves as digital archivists, they are somewhat unreliable for actual preservation. However, the ongoing existence of their community, despite the illegal, anonymous, and ephemeral nature of their work, invites one to consider the merits of a knowledge propagation model based on dissemination over preservation.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.695
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.002
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.028
GPT teacher head0.248
Teacher spread0.219 · 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 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

Citations1
Published2014
Admission routes1
Has abstractyes

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