La nature du progrès technique et la substitution des facteurs dans les pays en voie de développement
Bibliographic record
Abstract
The first part of the paper presents a new typology of direction of technological progress which is more suited to the problem of employment in developing countries, and factor substitution effects. An alternative to elasticity of substitution is proposed as providing more insights into the employment problems, namely the "range of substitution". The main conclusions here are that labour-saving technological progress as usually defined does not necessarily mean a reduced scope for substitution, contrary to the popular view of the technological determinist, but that nevertheless the most desirable direction of progress is in the neighborhood of the labour-intensive ridge line. The second part of the paper then considers how progress can be so directed. First, technological progress is narrowly defined not as a shift of the isoquant but as a movement of a particular process-point on the isoquant, reflecting the practical nature of R&D. Concluding that new technology is the result of efforts and resources denoted to a process, the paper infers from that the following policy implication: to improve employment opportunities, R&D resources must be oriented to labour-intensive processes, either by incentives or more explicitly. As a cautionary note, it is suggested factor prices do nevertheless matter, and technical efficiency is still an important criterion.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".