Comparaison internationale de l’utilisation de la main-d’oeuvre dans l’industrie : un programme linéaire
Bibliographic record
Abstract
This linear programming model for educational planning, by allowing for choice among techniques of production, permits the introduction of non-constant factor substitution into the production function. The model is applied to educational planning in France and treats simultaneously four kinds of educated manpower and capital in the seven major industrial sectors of an economy. Alternative techniques are drawn from seven other countries for which reasonably comparable data are available. These techniques of production define the production function and determine the demand for educated manpower and capital independently of the supply of these factors. An initial static model maximizes GNP (holding its composition constant) subject to a fixed supply of manpower and capital. The model thus tests whether supply is the constraining factor in the choice of technique in theshort run. In the case tested, it is. In the dynamic version of the model, supply is allowed to increase by means of education (for manpower) and investment (for physical capital). Consumable GNP, that is GNP net of the cost of education and investment, is maximized. Terminal capital stock problems make it impossible to test the model directly. The problem is then broken down into two steps: the identification of the techniques (one for each industry) which permit the greatest net contribution to GNP, and the movement in time towards these "optimal" techniques. The first of these steps is solved using a dual version of the model, but the second is not attempted in this paper.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".