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Public Infrastructure and the Productive Performance of Canadian Manufacturing Industries

2004· article· en· W4246373074 on OpenAlexaffabout
Satya Paul, Balbir S. Sahni, Bagala P. Biswal

Bibliographic record

VenueSouthern Economic Journal · 2004
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFiscal Policy and Economic Growth
Canadian institutionsEmployment and Social Development CanadaConcordia University
Fundersnot available
KeywordsPublic infrastructureProductivityPublic capitalCapital (architecture)Industrial organizationEconomicsBusinessDual (grammatical number)Public sectorManufacturingLabour economicsProduction (economics)MicroeconomicsEconomyMacroeconomicsMarketing

Abstract

fetched live from OpenAlex

This article examines the effects of public infrastructure on the productive performance of 12 two‐digit Canadian manufacturing industries. A flexible cost function incorporating public capital infrastructure is estimated for each industry separately using annual time series data for 1961‐1995. The effects of public infrastructure on productivity are measured in terms of both cost‐saving (dual) and output‐augmenting (primal) measures. We also investigate how public capital influences the input demand and cost structure in each industry and calculate the rate of return to public capital. The empirical results provide strong evidence of the important role public infrastructure plays in the productivity of manufacturing industries. The public capital serves as a substitute for both private capital and labor in most industries. The rates of return to public capital are significant and vary over the years.

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)0.005
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 score0.974
Threshold uncertainty score0.187

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0020.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.026
GPT teacher head0.173
Teacher spread0.146 · 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

Citations7
Published2004
Admission routes2
Has abstractyes

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