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Record W4232232505 · doi:10.24908/iqurcp.8958

Demographic Changes Affecting Our Pensions

2016· article· en· W4232232505 on OpenAlexvenueaboutno aff
Karicia Quiroz

Bibliographic record

VenueInquiry Queen s Undergraduate Research Conference Proceedings · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Policy and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsPillarPensionWorkforcePaymentBaby boomGovernment (linguistics)Labour economicsInvestment (military)BusinessRetirement ageEconomicsDemographic economicsFinanceEconomic growthPolitical sciencePopulationEngineeringMedicine

Abstract

fetched live from OpenAlex

Our Canadian retirement system contains three pillars, focusing on providing all members of Canadian society with a minimum and guaranteed standard of living for retirement, throughdirect transfers to help the working poor (first pillar) to Canada Pension Plan payments through mandatory monthly deductions (second pillar) funding current retirees and through taxdeductions offered via Canadians’ private savings (third pillar). Yet, with the baby boom generation presently retiring, and the current workforce positively shrinking, how will our monthly paycheck deductions towards Canada Public Pension payments be affected? Did you know that when you are born in 1985, you receive approximately $0.70 in retirement back from the government for every $1 contribution made from your current paycheck, and $0.60 back for every $1 contribution if born in 1995? These demographic changes are negatively affecting the future returns of our Canada Public Pension payments, despite a +$150 billion Canada Pension Plan Investment fund and the other two pillars of the Canadianretirement system. Either one improves the second pillar as it is, affecting the other two pillars, or one completely replaces that system with alternative options. This presentation will focus on the alternative options available and what can be done to mitigate the negative effects of the present demographic changes affecting the status of our Canada Public Pensions.

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)0.004
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.779
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.152
GPT teacher head0.409
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.

Study designTheoretical or conceptual
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
Published2016
Admission routes2
Has abstractyes

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