Bibliographic record
Abstract
One of the most significant post-war development in economics has been the contribution of econometrics to the refinement of techniques of analysis. Econometrics is now an integral part of economics teaching in most of the known universities throughout the world and large econometric models are being used for economic policies in industrialized countries. The CANDIDE model (Canadian Disaggregated Inter-Departmental Econometric Model) is a medium-term policy oriented model and an indication of some degree of maturity in the Art of model building in CANADA. The purpose of this special issue of L'Actualité Économique is to initiate undergraduate students in economics as well as the interested public to the model, to show how the model can be applied and finally to discuss some of its deficiencies. It is hoped that this special issue will be a useful tool for the teaching of econometrics in Canada. It has three parts. The first part comprising three papers, explains the nature and the characteristics of the model. The second part through five papers shows various applications of the model. Finally, in the third part, seven papers discuss some of the deficiencies of the major blocks of the model1. 1 Only the first two parts are included in this issue. The third part will be published in the next issue.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.463 | 0.290 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".