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Record W2524215773 · doi:10.7202/1069244ar

IMPLEMENTING GLOBAL PUBLIC INTERESTIN INFORMATION SOCIETY

2020· article· en· W2524215773 on OpenAlexaffvenue
Ram S. Jakhu

Bibliographic record

VenueRevue québécoise de droit international · 2020
Typearticle
Languageen
FieldEngineering
TopicICT Impact and Policies
Canadian institutionsMcGill University
Fundersnot available
KeywordsSummitInformation societyPovertyBridging (networking)BusinessPublic interestDigital dividePublic relationsTelecommunicationsPolitical scienceEconomic growthInformation and Communications TechnologyComputer securityComputer scienceEconomics

Abstract

fetched live from OpenAlex

Because the means of communication through which information is conveyed are indispensable for the eradication of poverty, public interest requires that all information and communications technology services must be available at affordable cost in all countries and to all areas within a country. Unfortunately, in spite of numerous international efforts, a digital divide still exists at both the national and international levels. Bridging this divide will be extremely difficult in the near future if the international and national regulatory regimes and approaches applicable to means of communications are not revised. There are many challenges, including access to appropriate national communication facilities, the privatisation of international operators in the field of satellite communications, the provision of domestic services by foreign operators, and the lack of national regulatory frameworks. To improve the situation, the Tunis Phase of the WSIS should ensure that all States and relevant international organisations follow the results of the Summit, and should focus on increasing the human intellectual capacity in regulatory matters.

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)0.033
metaresearch head score (Gemma)0.036
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.042
Threshold uncertainty score0.174

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0330.036
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0060.010
Scholarly communication0.0200.014
Open science0.0020.013
Research integrity0.0130.008
Insufficient payload (model declined to judge)0.0420.005

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 head0.017
GPT teacher head0.248
Teacher spread0.231 · 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 designTheoretical or conceptual
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

Citations1
Published2020
Admission routes2
Has abstractyes

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