Un modèle intersectoriel incluant une fonction d’investissement et des coefficients techniques variables
Bibliographic record
Abstract
Input-output analysis was always criticized for its inability to simulate all the effects produced by economic development; induced investment and its impact was notably one of the most serious lack usually noted. The model presented here is an attempt to prove that such a problem might well be solved in the future by introducing an investment function in the analysis at reasonable costs. By the same token, it tries to sell the possibility of taking into account the technological changes that occur in various industrial sectors, in allowing technical coefficients to change accordingly. The authors first briefly describe the economic rationale supporting the necessity of introducing such modifications in the static input-output analysis. Then, using the 1966 Quebec Input-Output table as the basic structure of their model, they formulate what could be presented as a fully dynamic (auto-regressive) model that can simulate the main effects that should be evaluated in an impact study: direct and indirect effects, and effects related to induced consumption and investment. Finally, running the model from a fictious variation in final demand and for a ten-year period, they conclude with the following results: 1°) the introduction of the accelerator increases by about 30% (the figure varies from 65% to 15% during the period) the impact that would have been otherwise obtained with the static model; and 2°) the use of actual technical coefficients (the introduction of technological changes) reduces by 20% the impact that would have been estimated without the modification.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".