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Record W2099878124 · doi:10.3138/cpp.33.2.127

The Design of Regulatory Institutions for the Canadian Telecommunications Sector

2007· article· en· W2099878124 on OpenAlexaffvenueabout
Edward Iacobucci, Michael J. Trebilcock

Bibliographic record

VenueCanadian Public Policy · 2007
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMerger and Competition Analysis
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsTribunalCompetition (biology)CommissionAgency (philosophy)Regulatory reformTelecommunicationsMarket powerBusinessExploitEconomicsIndustrial organizationLawMarket economyFinancePolitical scienceComputer securityComputer science

Abstract

fetched live from OpenAlex

As the result of competition arising from new technology, extensive economic regulation of the telecommunications industry has become less appropriate over time. In this article we consider corresponding institutional reform. Both the Canadian Radio-television and Telecommunications Commission (CRTC) and the Competition Bureau/Tribunal are presently involved in telecom regulation. We propose a framework in which there is a clearer division of responsibility between the CRTC and the Bureau/Tribunal. The latter would be responsible for enforcing laws against predatory pricing, price discrimination, and other standard competition policy matters in the telecom industry. Where there is concern simply about high prices, a matter that competition policy does not ordinarily address directly, we propose that the Bureau/Tribunal assume responsibility for identifying markets in which there is market power, and only then would the CRTC have the authority to regulate prices. We argue that such an arrangement would allow each agency to exploit its comparative advantage, would reduce costly duplication across agencies, and would address concerns about regulatory overreach.

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)0.019
metaresearch head score (Gemma)0.030
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.794
Threshold uncertainty score0.921

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.030
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.005
Science and technology studies0.0130.014
Scholarly communication0.0130.004
Open science0.0030.003
Research integrity0.0050.004
Insufficient payload (model declined to judge)0.0050.001

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 head0.104
GPT teacher head0.267
Teacher spread0.163 · 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 designNot applicable
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

Citations14
Published2007
Admission routes3
Has abstractyes

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