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Record W7095105066

The Pension Factor of 9: Actuarial Logic Skewed by Political Myopia

2014· article· en· W7095105066 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Ecosystems and Biodiversity
Canadian institutionsnot available
Fundersnot available
KeywordsPensionDeductibleSavings accountGovernment (linguistics)RevenuePoliticsInvestment (military)Tax deductionField (mathematics)
DOInot available

Abstract

fetched live from OpenAlex

This paper provides a quick review of the Actuarial Factor of “9”. This factor was devised as an attempt to create a level playing field between Defined Benefit and Defined Contribution Pension Plans (and with Registered Retirement Savings Plans). The paper argues that this attempt would have been quite successful had it not been for the “freezing ” of the CCRA contribution limits many, many times. The paper argues that these limits have been “frozen ” because the Ministry of Finance sees RPPs/RRSPs as representing a large “tax expenditure”. This is because RPP/RRSP contributions are tax deductible and investment income on Registered funds accrues tax free until taken as income. The paper argues that instead of being viewed as “tax expenditures”, RPP/RRSPs represent the “perfect ” deferred tax asset. This is because the Canadian government will reap increased tax revenues from the baby-boom generation when they cash in their RPP/RRSPs, which will be exactly when the government will need extra funds to pay for increased health care services for the same baby-boom generation. The paper goes on to argue that the CCRA contribution limits (which are “frozen”) also create a bias in favour of DC plans versus DB plans. While this may be intended, the author doubts that it is understood by Ottawa parliamentarians. In summary, the paper argues that it is time to index the pension contributions to average wages, as was originally intended, to restore the actuarial logic of the factor of “9”. 1

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.255
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.010
GPT teacher head0.201
Teacher spread0.191 · 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.

Study designObservational
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

Citations0
Published2014
Admission routes1
Has abstractyes

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