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
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".