Diagnostic des PME québécoises qui n'offrent pasde régimes de retraite à leurs employés
Bibliographic record
Abstract
Sommaire Peu de recherches scientifiques se sont preoccupees d'analyser les causes de l'absence deregimes de retraite pour les employes des PME qui emploient de 1 a 300 personnes. L'objet de cette etude estde combler cette lacune. L'enquete aupres des 1,009 repondants montre que le phenomene d'absence de regimesde retraite aux employes touche plus specifiquement les PME de plus petite taille, fragilisees par la faiblesse deleur chiffre d'affaires, la faiblesse des revenus de leurs employes ainsi que leur jeune age. Une analyse deregroupement a fait ressortir, au sein de notre echantillon, quatre groupes de PME sur la base des beneficespercus dans un regime de retraiteand de l'intention d'en implanter. Puis, une analyse de regression logistique aensuite permis de determiner, parmi les facteurs identifies dans l'analyse de regroupement, ceux qui ont le plusd'importance dans la decision d'implanter ou non un regime de retraite. Ainsi, il ressort de la presente etudequ'afin de promouvoir la securite financiere a la retraite des travailleurs des PME, on devrait prioritairementcibler les entreprises qui sont en croissance, qui comptent une faible proportion d'employes ayant des revenusinferieurs a 20 000 $,and qui offrent deja un regime d'assurance collective a leurs employes.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".