Un modèle intersectoriel de l’économie canadienne avec contrainte sur l’offre; une approche utilisant la programmation linéaire
Bibliographic record
Abstract
This article describes a model, developed by the Structural Analysis Division of Statistics Canada, that helps analyse the economic implications of policy decisions in the environment of a supply-constrained economy. The Canadian input-output model is modified to introduce constraints on the uses of some commodity or industry products. These constraints take the form of limits on the availability of commodities for some uses, constraints that ensure that some minimum levels of final demand for each commodity are satisfied, and capacity constraints on the outputs of industries. Given these constraints, a linear function of the activity levels is maximized. The resulting solution gives a vector of activity levels, and also corresponding final demands that are optimal in terms of the objective function. The use of the model is illustrated by analyzing the 'optimal' allocation of industrial outputs in the face of a reduction in the availability of the commodity, 'crude mineral oils', for industrial uses. Two objective functions are used: total employment, and total wages, salaries and supplementary labour income. For each objective function, a ranking of the industries is defined by the solutions of the model. Experience with this model leads us to conclude that it is useful in indicating which industries are of primary interest in a specific shortage situation, rather than in setting exact values of cutbacks to impose on industries. In the conclusion, relaxation of the major assumptions underlying the model and some possible extensions are discussed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.001 |
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.
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".