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Record W7098175244

CARP- A New Vision of Aging for Canada | www.CARP.ca | 1-888-363-2279 Nov 2014 Eliminating Mandatory RRIF Withdrawals

2015· article· en· W7098175244 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine Invertebrate Physiology and Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsPensionPaceRetirement ageSocial securityAnnuityMandatory retirementLife annuity
DOInot available

Abstract

fetched live from OpenAlex

RRIF Rules undermine retirement security Canadians are not saving enough for their own retirement and there is growing concern that many will face considerable income insecurity in retirement.i Current rules mandating minimum yearly withdrawals from Registered Retirement Income Funds (RRIFs) compound the problemii since Canadians who have retirement savings in Registered Retirement Savings Plans (RRSPs) are compelled under the current rules to drawdown their savings starting at age 71- when RRSPs must be converted to Registered Retirement Income Funds (RRIFs). These rules are designed to virtually empty out the RRIF by age 92, thereby placing many Canadians at risk of outliving their retirement savings given today’s longevity rates. RRIF rules have not kept pace with increasing lifespans and time spent in retirement, declines in personal savings rates, and reduced access to workplace pension plans. When the original RRIF rules and withdrawal rates were introduced in 1978 and then increased in 1992, lifespans and time spent in retirement were much shorter than today and Canadians on average spent less time in retirement. RRIF holders now face considerable likelihood of running out of money in late stages of retirement. CARP calling for an elimination of mandatory RRIF withdrawals

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.143
Threshold uncertainty score0.479

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0120.004
Scholarly communication0.0080.003
Open science0.0030.005
Research integrity0.0070.005
Insufficient payload (model declined to judge)0.1430.036

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.

Opus teacher head0.014
GPT teacher head0.226
Teacher spread0.212 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2015
Admission routes1
Has abstractyes

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