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Record W1572491869

Reductions tarifaires et emploi dans le secteur de la fabrication au Canada, 1988 a 1994

2005· preprint· fr· W1572491869 on OpenAlexaboutno aff
Sebastien Larochelle-Côté

Bibliographic record

VenueRePEc: Research Papers in Economics · 2005
Typepreprint
Languagefr
FieldEconomics, Econometrics and Finance
TopicFirm Innovation and Growth
Canadian institutionsnot available
Fundersnot available
KeywordsPolitical scienceHumanitiesArt
DOInot available

Abstract

fetched live from OpenAlex

Cette etude utilise les donnees sur les entreprises du T2/PALE pour examiner si le lien entre les modifications tarifaires et l'emploi s'est manifeste de facon differente d'une entreprise a l'autre affichant des caracteristiques de productivite et de levier financier differentes au cours de la periode 1988 a 1994. Les resultats indiquent que l'effet combine des reductions tarifaires au Canada et aux Etats-Unis sur l'emploi a ete peu eleve, mais que les pertes ont ete beaucoup plus importantes au sein des entreprises moins productives. Par exemple, les entreprises a productivite moyenne en 1988 ont reagi aux modifications tarifaires en diminuant leurs effectifs de seulement 3, 6 % entre 1988 et 1994, alors que les entreprises de plus faible productivite ont typiquement diminue leurs effectifs de 15, 1 % au cours de cette periode. L'etude indique aussi que les entreprises plus endettees ont davantage reduit leurs effectifs a la suite de la diminution des tarifs, ce qui demontre que les conditions de credit sont devenues plus contraignantes au moment de la mise en application des diminutions tarifaires. Ces resultats semblent indiquer que les entreprises a productivite elevee et a faible endettement ont ete moins susceptibles que les autres de ressentir les effets de la diminution des tarifs canadiens et americains.

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)0.001
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 score0.026
Threshold uncertainty score0.189

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.025
GPT teacher head0.278
Teacher spread0.253 · 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
Published2005
Admission routes1
Has abstractyes

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