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Record W2134252927 · doi:10.3917/rfea.134.0081

The Fifth Estate in Internet Governance: Collective Accountability of a Canadian Policy Initiative

2013· article· fr· W2134252927 on OpenAlexaboutno aff
Elizabeth Dubois, William H. Dutton

Bibliographic record

VenueRevue française d’études américaines · 2013
Typearticle
Languagefr
FieldComputer Science
TopicCybercrime and Law Enforcement Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPolitical scienceHumanitiesArt

Abstract

fetched live from OpenAlex

Résumé Le public a rarement joué de rôle important dans la gouvernance d’Internet. Le cas du projet de loi C30 (Bill C30), projet présenté en 2012 pour mettre à jour la politique canadienne concernant les enquêtes visant les communications électroniques, est un exemple de participation citoyenne en ligne à l’élaboration de la gouvernance et de la régulation d’Internet. L’article analyse le rôle qu’Internet a joué dans la façon dont les citoyens ont pu influencer le destin du projet C30 pour en faire une question de gouvernance. L’exemple porte sur la dynamique du « Cinquième pouvoir », force sociale composée de réseaux de particuliers comparable à la presse d’un autre temps, le Quatrième pouvoir. En redéfinissant le débat comme étant autour de la surveillance et de la vie privée, et non plus de la criminalité, les réseaux de particuliers ont été capables de valoriser leur pouvoir de communication et de s’opposer au projet C30. Si certains restent sceptiques quant au pouvoir des réseaux de particuliers et à l’émergence d’un Cinquième pouvoir, ce cas d’étude met en évidence l’importance de ces évolutions.

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)0.009
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.206
Threshold uncertainty score0.921

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.005
Science and technology studies0.0290.016
Scholarly communication0.0210.005
Open science0.0020.006
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0130.000

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 head0.038
GPT teacher head0.262
Teacher spread0.224 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations24
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueRevue française d’études américainesSame topicCybercrime and Law Enforcement StudiesFrench-language works237,207