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Record W2979458673 · doi:10.1093/isp/ekz016

Online Surveillance, Censorship, and Encryption in Academia

2019· article· en· W2979458673 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Studies Perspectives · 2019
Typearticle
Languageen
FieldComputer Science
TopicDigital Education and Society
Canadian institutionsQueen's UniversityUniversity of Toronto
FundersAgence Nationale de la Recherche
KeywordsAcademic freedomCensorshipScholarshipThe InternetPolitical sciencePublic relationsConventionDigital scholarshipSociologyHigher educationInternet privacyLawLibrary scienceWorld Wide WebComputer science

Abstract

fetched live from OpenAlex

Abstract The Internet and digital technologies have become indispensable in academia. A world without email, search engines, and online databases is practically unthinkable. Yet, in this time of digital dependence, the academy barely demonstrates an appetite to reflect upon the new challenges that digital technologies have brought to the scholarly profession. This forum's inspiration was a roundtable discussion at the 2017 International Studies Association Annual Convention, where many of the forum authors agreed on the need for critical debate about the effects of online surveillance and censorship techniques on scholarship. This forum contains five critiques regarding our digitized infrastructures, datafied institutions, mercenary corporations, exploitative academic platforms, and insecure online practices. Together, this unique collection of articles contributes to the research on academic freedom and helps to frame the analysis of the neoliberal higher education sector, the surveillance practices that students and staff encounter, and the growing necessity to improve our “digital hygiene.”

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.694
Threshold uncertainty score0.259

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.023
GPT teacher head0.338
Teacher spread0.315 · 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