Bibliographic record
Abstract
In this paper, the technological progress is measured by the relative change in total unit costs when input prices are constant and when the scale of production is optimal, that is where marginal cost intersects with average cost. The conceptual framework for a measurement of technological change is presented in the first section. Optimal scale and minimum average cost are therein illustrated, to elaborate, afterwards, on the decomposition of technical change in three major components. The approach to analyze the time pattern of substitution possibilities is given attention to as well as the analysis of heterotheticity and of various types of technological biases. The second section deals briefly with the econometrics of technology estimation on a sectoral basis. The technology is modelled through a "translog" cost function, that proves to be a second order approximation of any cost function. It is worthwhile to point out that this function may exhibit timevarying substitution elasticities as well as variable returns to scale. A brief discussion of the data used follows. The methodology was applied to Electrical and Chemical product industries, at the three digits level. In the third section, the empirical results are analyzed. They allowed to characterize the substitution profile, the scale and technological biases. They lead, also, to a decomposition analysis of technological change in three major components: efficiency effect, scale effect and bias effect. The analysis was related to 16 subsectors in the Canadian manufacturing, over the period 1961-1976.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".