Le progrès, le transfert et le choix technologiques dans les pays en voie de développement (PVD) : vers une approche plus réaliste du problème de la substitution
Bibliographic record
Abstract
In this paper it is argued that the conventional estimation of production functions may be misleading for the study of technological progress and technology choice in developing countries. The analysis of technical progress and technology transfers requires empirical production functions which should reflect accurately the state of technology and productivity in a given country or region. The neoclassical production function embracing as an envelope all observations in an industry is likely to overstate the number of techniques already established in the region. It may therefore underestimate the technical progress achieved by further transfers. The problem lies in the very concept of technological progress which is defined as a shift of the universal production function and excludes movements along the production isoquant. As to the choice of technology the estimation of the elasticity of substitution may be equally misleading for the purpose of predicting changes of factor use caused by factor price changes. The substitution possibilites between factors of production in the actually existing choice set of techniques for a given country or region are better described by the concept of a technology shelf. Two important characteristics of the technology shelf are the range and density of substitution. It is argued that industrial engineering information should be used to better describe the technology shelf. In its empirical part, using data of the manufacturing sector of Zaire, the paper shows how such engineering information may be used to estimate the range and density of substitution.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".