MétaCan
Menu
Back to cohort
Record W2990843083 · doi:10.1109/mahc.2019.2896282

The Development of Consent to Computing

2019· article· en· W2990843083 on OpenAlexaboutno aff
Meg Leta Jones

Bibliographic record

VenueIEEE Annals of the History of Computing · 2019
Typearticle
Languageen
FieldComputer Science
TopicHistory of Computing Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsNegotiationTelematicsHistory of computingTransparency (behavior)PoliticsDigital transformationWork (physics)Political scienceComputer scienceLawTelecommunicationsEngineering

Abstract

fetched live from OpenAlex

The origins and transformation of digital consent are recounted in a comparative fashion, focusing on political constructions of computing in Western countries, regional bodies, and global negotiations. When data protection regimes emerged to govern computing technologies in the 1970s, the U.S., Canada, the U.K., France, and Sweden all ignored consent, but for very different reasons, and structured the governance of computers in related but diverse ways. Germany's unique construction of computing as a particular moral act that required consent would later find an interesting bedfellow with the U.S., which had relied heavily on transparency as a policy tool, as national systems gave way to international entities establishing rules for telematics, transnational data flows, and a newly individualized computer revolution in the 1980s and 1990s. This work contributes to a growing body of work on both the history of globalized communication and the legal history of computing.

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 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)0.022
metaresearch head score (Gemma)0.037
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.993
Threshold uncertainty score0.114

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.037
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0070.087
Scholarly communication0.0140.019
Open science0.0020.008
Research integrity0.0060.009
Insufficient payload (model declined to judge)0.0060.001

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 head0.092
GPT teacher head0.296
Teacher spread0.204 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designQualitative
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

Citations7
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueIEEE Annals of the History of ComputingSame topicHistory of Computing TechnologiesFrench-language works237,207