RSI-02: Financial Products for Retirement / Produits financiers 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 Enquête déployée en juin 2017 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 3 001 répondants de l’Ontario, du Québec et de la Colombie-Britannique âgés de 55 à 75 ans Questions portant sur Les caractéristiques socioéconomiques Opinions et perceptions par rapport aux intentions de legs, au risque de longévité, à l’aversion au risque et au rôle de la famille pendant la retraite La littératie financière et les connaissances Connaissance des rentes et des hypothèques inversées Probabilité d’acheter des rentes ou des hypothèques inversées ayant des caractéristiques variant selon le scénario 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 Survey fielded in June 2017 by Delvinia's AskingCanadians web panel, on behalf of members of the Retirement and Savings Institute 3,001 respondents from Ontario, Quebec, and British Columbia, aged between 55 and 75 years old Questions on Opinions and perceptions on bequest motives, longevity risk, risk aversion, and the role of family in retirement Financial literacy and knowledge Knowledge of annuities and reverse mortgages Probability of buying annuities or reverse mortgages with characteristics that vary in each scenario
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.441 | 0.318 |
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".