Évaluation du risque de crédit de travailleurs autonomes : le cas d’une caisse populaire Desjardins du Québec
Bibliographic record
Abstract
Dans cet article, nous analysons les facteurs qui commandent le risque des prêts consentis à des travailleurs autonomes par une institution financière, en l’occurrence une caisse populaire. Après avoir considéré l’approche traditionnelle−i.e. l’analyse discriminante−, nous nous tournons vers un cadre d’analyse qui présente l’avantage d’intégrer des éléments qualitatifs reliés au jugement du décideur : l’analyse multicritère. Nos résultats montrent que l’analyse multicritère serait supérieure à l’analyse discriminante en termes de classification des bons et des mauvais prêts. Toutefois, l’analyse multicritère reposant sur le jugement, une certaine prudence s’impose au chapitre du recours à cette méthode.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.043 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.010 | 0.025 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads 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".