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The Merger between Rogers and Shaw

2023· article· en· W4386686784 on OpenAlexaffabout
Xi Chen

Bibliographic record

VenueAdvances in Economics Management and Political Sciences · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Policy and Governance
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsGovernment (linguistics)LivelihoodRemedial educationPublic serviceService (business)Public policyAction (physics)WelfareEconomicsBusinessTelecommunicationsPolitical sciencePublic relationsEngineeringMarket economyEconomyEconomic growthLaw

Abstract

fetched live from OpenAlex

The communication industry is an essential issue of people's livelihood, which has re-ceived the attention of government regulators and the public. As one of the largest mer-gers in Canada, the merger between Rogers and Shaw caused too much attention from the public. This paper discussed the status of the Canadian Telecommunication industry, the reasons why Canadian Government approved the merger, the potential risks of the merger to the participants in the communication market and the influence of Rogers’ service dis-ruption on national business and the merger. In addition, on the basis of macroeconomic theories, the author provides some remedial and preventive measures to combat similar future risks as well as the short-term and long-term effects of restrictions on the merger. At the last, the paper claims that the merger will increase the market power of Rogers, and it requires Canadian government to take action to regulate the telecommunication industry to improve people’s welfare because it closely connects with everybody’s life.

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)0.006
metaresearch head score (Gemma)0.018
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 score0.693
Threshold uncertainty score0.611

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0120.028
Scholarly communication0.0140.008
Open science0.0010.004
Research integrity0.0080.012
Insufficient payload (model declined to judge)0.0040.001

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 head0.020
GPT teacher head0.315
Teacher spread0.296 · 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

Citations0
Published2023
Admission routes2
Has abstractyes

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