La méthodologie des modèles intersectoriels rectangulaires à coefficients modifiables : rétrospective et perspective
Bibliographic record
Abstract
The best known and probably the most frequently used of the models of this class is undoubtedly the Input-Output model of the Québec economy built, continuously updated and operated by the Bureau de la Statistique du Québec. However, the methodology, adapted and sometimes extended has found a number of other, in particular micro-economic applications. Evidently, the basic inspiration of the methodology in question is to be found in the ideas put forward by Professor Leontief. Certain researches done in France, especially in the late 1950's, but also since then, have exerted considerable influence. Although these models trace their origins to activity analysis in the sense that they start from the principle that to understand a complex system it is preferable to study in detail its inner structures and workings rather than the evolution over time of the great aggregates characterising the overall behaviour. By abandoning the postulates of proportionality and of one-to-one correspondence between "products" and "industries", the models discussed here openly give up any pretence to mathematical elegance including the existence of "general solutions" of the kind of those associated with the Leontief inverses. They just become in effect simulation models and at the same time much more convenient and flexible frameworks for the collection, organization and the handling of data, data which are much closer to basic data than the highly processed data incorporated in the traditional Input-Output models. They are also much more easier to update and to incorporate "non-statistical" data. Although more powerful, in many respects, than the traditional models, they share with them at least two basic weaknesses which, significantly, are not unrelated to each other. They are incapable of handling in a really comprehensive and systematic manner the confrontation of supply and of demand influences and they give no more than a most cursory treatment to the whole range of financial phenomena and a fortiori to the influence of these phenomena on the "real" ones. It is clear that the future work on this class of models will have to put heavy emphasis on trying to reduce these two weaknesses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".