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Record W1561679426 · doi:10.7202/602324ar

L’impact de l’annonce de la privatisation sur la performance

2009· article· fr· W1561679426 on OpenAlexaffvenueabout
Yves Bozec, Claude Laurin

Bibliographic record

VenueL Actualité économique · 2009
Typearticle
Languagefr
FieldBusiness, Management and Accounting
TopicTransport and Economic Policies
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsPolitical scienceHumanitiesArt

Abstract

fetched live from OpenAlex

Cet article vise à analyser si le statut juridique de l’entreprise et la nature des objectifs qu’elle poursuit influent sur l’efficience de ses opérations. Pour ce faire, nous avons choisi de comparer la productivité des deux principaux transporteurs ferroviaires canadiens, soit le Canadien National (CN), transporteur du secteur public, avec son plus proche rival du secteur privé, le Canadien Pacifique (CP), durant les périodes précédant et suivant l’annonce de la privatisation du CN. L’efficience interne des deux transporteurs est comparée à l’aide de la productivité totale des facteurs (PTF) sur une période de quinze ans, soit de 1981 à 1995. Les résultats tendent à démontrer que bien qu’étant moins efficientes durant la période 1981-1991, les opérations du CN sont devenues aussi efficientes que celles du CP durant la période de préprivatisation, soit de 1992 à 1995. Ces résultats nous portent à conclure qu’un changement dans la nature des objectifs poursuivis par le CN a eu un impact significatif sur son efficience interne.

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)0.004
metaresearch head score (Gemma)0.008
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: Empirical
Teacher disagreement score0.064
Threshold uncertainty score0.126

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0000.001
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 head0.020
GPT teacher head0.237
Teacher spread0.217 · 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

Citations1
Published2009
Admission routes3
Has abstractyes

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