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Record W6908917626 · doi:10.34989/tr-17

The Canadian Newsprint Industry: Econometric Models of Different Market Structures

2024· article· en· W6908917626 on OpenAlexaboutno aff

Bibliographic record

VenueBank of Canada Research · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMerger and Competition Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsNewsprintMarket structureEconometric modelEstimationSet (abstract data type)Order (exchange)Perfect competition

Abstract

fetched live from OpenAlex

The primary purpose of this report is to determine the type of market structure that best describes the Canadian newsprint industry. Accordingly, a set of models corresponding to a perfectly-competitive, dominant-firm and an "equal-partner cartel" industry structure are simulated. The procedure involves the estimation of a set of equations explaining various economic aspects of the industry. Different subsets of the equations are then employed as analogues for each market structure considered. These subgroups of equations are evaluated and compared by analyzing both intra- and extra-sample simulations. Exchange rate and GNP shocks are then used to compare the dynamics of some of the models. Unfortunately, the analysis does not reveal a universally superior model. While the competitive model seems to perform best over the estimation period, its forecasts are inferior to those of some of the imperfectly-competitive models. It would appear therefore, that further refinements in the model and equation specifications are required in order to resolve the market structure issue.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.732
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.070
GPT teacher head0.273
Teacher spread0.203 · 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.

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

Citations0
Published2024
Admission routes1
Has abstractyes

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