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Record W137213734

Pooled Registered Pension Plans: Pension Saviour - Or a New Tax on the Poor?

2012· article· en· W137213734 on OpenAlexaboutno aff
James Pierlot, Alexandre Laurin

Bibliographic record

VenueSSRN Electronic Journal · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Policy and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsPensionLegislationBusinessJurisdictionAsset (computer security)Administration (probate law)Private pensionSavings accountPoolingLabour economicsActuarial scienceFinanceEconomicsPolitical science
DOInot available

Abstract

fetched live from OpenAlex

In June 2012, the regulatory framework for a promising new retirement savings vehicle, Pooled Registered Pension Plans (PRPPs), was passed into Canadian federal legislation. The hope is that PRPPs will improve pension coverage and retirement-saving outcomes by reducing costs and improving investment returns through asset pooling and third-party administration. Since most employers under federal pension legislation are already providing pension coverage to their employees, PRPPs were introduced in the expectation that provincial governments would follow the federal lead and adopt PRPPs for the vast majority of Canadian workers under provincial pension jurisdiction. As of now, only the province of Quebec has announced its intention to create its own distinct version of PRPPs, branded Voluntary Retirement Savings Plans. Although the intentions behind PRPPs are commendable, PRPPs represent only a mild improvement over existing options such as Registered Retirement Savings Plans (RRSPs) and defined-contribution (DC) pension plans. This is because tax rules for PRPPs – essentially identical to those that apply to RRSPs and similar to those for DC plans – will prevent many private-sector workers from saving enough for retirement and from receiving retirement income in the form of a life pension.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.712
Threshold uncertainty score0.952

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.040
GPT teacher head0.297
Teacher spread0.257 · 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 teacher head, not a consensus.

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

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

Citations6
Published2012
Admission routes1
Has abstractyes

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