Appréciation multidimensionnelle des distributions de statut économique
Bibliographic record
Abstract
It is today hugely accepted that the economic status of the individuals and the social welfare are in essence multidimensional phenomena. Despite this obviousness, economists have been recently interested in this topic and much applied comparisons of countries on the basis of their performance in achieving better redistributive objectives continue to be performed in terms of monetary income. Following seminal contributions of Kolm (1977, QJE) or Atkinson and Bourguignon (1982, RES) and the recent literature, the topic of this thesis is to show the relevance of the multidimensional appraisal of societies. In other words, the question is: what information does the multidimensional methodology provide us? This thesis tries to answer to this question through three contributions. The first one propose an international comparison of developed countries in terms of their normative performance in allocating disposable income and regional public goods (health and education) to their citizens based on robust multidimensional dominance criteria and we show that the picture of the relative standing of countries is significantly affected. The second contribution proposes a methodology for comparing socially risky situations form a normative point of view. This methodology allows us to say that, for single workers, the United States are not more precarious than France. Finally the purpose of the third contribution is to verify whether or not the Canadian equalization payments scheme succeeds in reaching its redistributive objective. We show that there are some doubts on the distributional accomplishments of the Canadian equalization payment scheme.
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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 | 0.013 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 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".