Une application expérimentale de la méthode de minimisation de l'entropie croisée: l'estimation des flux d'échanges interrégionaux au Québec
Bibliographic record
Abstract
Cet article présente une application expérimentale de la méthode d'estimation des flux d'échanges entre des régions par la minimisation de l'entropie croisée (mesure de Kullback-Leibler). Les flux interrégionaux ont été estimés pour 31 catégories de biens et services, entre trois régions du Québec en 1992: les régions métropolitaines de Montréal et de Québec et le Reste-du-Québec. La méthode, d'inspiration bayesienne, a permis de dégager des flux interrégionaux respectant les totaux marginaux provenant de matrices de comptabilité sociale régionales préexistantes, à partir de données sur les flux de transport jouant le rôle de distribution a priori./In this paper, we report on an experimental application of Kullback-Leibler cross-entropy minimization to estimate trade flows between regions. Interregional trade flows were estimated for 31 categories of goods and services, between three regions of the Province of Québec in 1992: the Montréal and Québec metropolitan areas, and the Rest-of-Québec. With this bayesian-style method, we were able to obtain interregional flows that respect the marginal totals taken from pre-existing regional social accounting matrices, using transportation flow data as a priori distributions.
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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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".