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Record W1517312946

Did the Canadian Newspaper Acquisitions Raise Prices for Consumers

2007· article· en· W1517312946 on OpenAlexaffabout
Allan Collard‐Wexler, Ambarish Chandra

Bibliographic record

VenueThe Faculty Digital Archive (New York University) · 2007
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMerger and Competition Analysis
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsNewspaperConsolidation (business)Circulation (fluid dynamics)Market powerDiversity (politics)EconomicsMergers and acquisitionsBusinessAdvertisingMonetary economicsMarket economyAccountingPolitical scienceFinanceMonopolyLaw
DOInot available

Abstract

fetched live from OpenAlex

In the late 1990s, the Canadian newspaper industry underwent rapid consolidation with a few conglomerates controlling the vast majority of daily papers. Over a 4 year period, about three-fourths of Canada’s daily newspapers changed ownership. While the issue re- ceived considerable attention and criticism at the time, the concerns were mostly about diversity of opinion. We have not found any study examining the straightforward economic implications of such a large scale realignment in this important industry. We examine the effect of this consolidation on observable variables relating to consumer welfare. Specifically, we analyze prices for both circulation and advertising, as well as study the extent to which concentration increased using county level circulation data. Our results do not support the notion that greater concentration led to the abuse of market power in the form of higher prices. In fact, our results suggest that newspapers with changed ownership and those in the dominant chains had either lower price increases or greater price declines after the merger, compared with the other papers.

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)0.001
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.053
Threshold uncertainty score0.223

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.002
Scholarly communication0.0040.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.043
GPT teacher head0.211
Teacher spread0.168 · 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 designObservational
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
Published2007
Admission routes2
Has abstractyes

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