RSI-03: Retirement Saving Vehicles / Véhicules d’épargne pour la retraite
Bibliographic record
Abstract
ON RECOMMANDE AUX UTILISATEURS DE COMMENCER PAR LIRE LE FICHIER «READ ME» INCLUS DANS L’ARCHIVE, QUI CONTIENT DES INFORMATIONS IMPORTANTES ET PEUT ÊTRE OUVERT AU FORMAT TEXTE BRUT AVEC LA PLUPART DES LOGICIELS D’ÉDITION. Résumé 3 005 répondants de l'Ontario et du Québec âgés de 35 à 55 ans Enquête déployée en mai et juin 2018 par le panel Web Qu'en pensez-vous? de la firme Delvinia, pour le compte de membres de l'Institut sur la retraite et l'épargne Questions sur les caractéristiques socioéconomiques, les habitudes de dépenses et les plans pour la retraite Aversion au risque et préférence pour le temps obtenues par l’entremise de choix avec incitations monétaires Intervention d’éducation financière à assignation aléatoire concernant les implications fiscales des comptes d’épargne à l’abri de l’impôt taxés à l’entrée ou à la sortie Expérience de sélection entre des comptes d’épargne à l’abri de l’impôt taxés à l’entrée ou à la sortie USERS ARE ADVISED TO START BY READING THE “READ ME” FILE INCLUDED IN THE ARCHIVE, WHICH CONTAINS IMPORTANT INFORMATION AND CAN BE OPENED IN RAW TEXT FORMAT USING MOST TEXT EDITING SOFTWARES. Summary 3,005 respondents from Ontario and Quebec, between 35 and 55 years old Survey fielded in May and June 2018 by Delvinia’s AskingCanadians web panel, on behalf of members of the Retirement and Savings Institute Questions on socioeconomic characteristics, spending habits and retirement plans Risk aversion and time preference elicited through incentivized choice Randomly assigned financial education intervention about the taxa implications of front- or back-loaded tax-sheltered saving accounts Choice experiment between front-and back-loaded tax-sheltered saving accounts
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.151 | 0.071 |
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".