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Record W1515405990 · doi:10.7202/800604ar

L’emploi de modèles intersectoriels rectangulaires à coefficients modifiables pour simuler la propagation de la demande pour les fins de la planification du développement industriel

2009· article· en· W1515405990 on OpenAlexaffvenue
Isabelle Bergeron, T. I. Matuszewski

Bibliographic record

VenueL Actualité économique · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicRegional Development and Policy
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsSimple (philosophy)NormativeWelfare economicsMathematical economicsComputer scienceOperations researchEconomicsEconomyMathematicsPolitical scienceEpistemologyPhilosophy

Abstract

fetched live from OpenAlex

This article presents a methodology which draws heavily on the philosophy of the Input-Output models and having been made completely operational has already been used on three occasions in two countries for the purposes of regional development of construction materials industries. This methodology, or more precisely the strictly formalized part of it is an extension of that of rectangular Input-Output models with modifiable coefficients. Thus, not surprisingly, it resembles fairly closely the approach by simulation, although the proposed model contains some simple optimizing sub-models. While obviously normative, these sub-models play a descriptive rôle in the model as a whole. It is to be noted that the approach presented here can be applied to only one sector of the economy at a time. What is more, although capable of various extensions it will never be more than an auxiliary instrument destined to be used jointly with other analysis and planning instruments. It is vital for any valid regional analysis not to restrict its investigations exclusively to what goes on in the region directly concerned. Even if the objective of the analysis is limited to a single region, one must take into account the interrelations between regions within the national economy and with foreign economies: important feedbacks affecting the region concerned may on occasion travel far beyond its limits before returning. The type of a model presented here, thanks to a great number of interrelations of which it can systematically keep track may turn out to be particularly useful here.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.556
Threshold uncertainty score0.772

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.051
GPT teacher head0.326
Teacher spread0.275 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations2
Published2009
Admission routes2
Has abstractyes

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