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Record W4300647503 · doi:10.18192/cjmsrcem.v14i1.6474

The Never-ending Story: Postmedia, the Competition Bureau, and Press Ownership in Canada

2016· article· en· W4300647503 on OpenAlexaffvenueabout
Marc Edge

Bibliographic record

VenueCanadian Journal of Media Studies · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicLaw, logistics, and international trade
Canadian institutionsUniversity Canada West
Fundersnot available
KeywordsNewspaperMonopolyCompetition (biology)CommissionPolitical scienceEconomic historyLawHistoryAdvertisingBusinessEconomicsMarket economy

Abstract

fetched live from OpenAlex

The 2014 purchase by Canada’s largest newspaper chain of its second-largest chain increased concentration of newspaper ownership considerably. The deal’s 2015 approval by the Competition Bureau, some scholars noted, provoked little outcry over the latest federal regulatory failure to stop the increased concentration level. A series of inquiries, from the 1981 report of the Royal Commission on Newspapers to Senate reports in 1970 and 2006, all identified increased concentration of newspaper ownership as a problem and proposed measures to solve it. Formed in the 1980s, the Competition Bureau took action against a local newspaper monopoly in Vancouver in the early 1990s but has been ineffective since. This study charts the historical progress of newspaper ownership concentration in Canada and calculates that Postmedia now publishes 37.6 percent of Canadian paid daily newspaper circulation and owns fifteen of the twenty-two largest Englishlanguage dailies. That includes 75.4 percent in the three westernmost provinces, where Postmedia owns eight of the nine largest dailies. Possible explanations for a lack of outcry include the company’s use of the “death of newspapers” meme as justification and the fact the deal’s effect was felt mostly in Western Canada, far from the corridors of power.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.501
Threshold uncertainty score0.422

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.061
GPT teacher head0.233
Teacher spread0.172 · 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 teacher head, 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

Citations2
Published2016
Admission routes3
Has abstractyes

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