MétaCan
Menu
Back to cohort
Record W3122900349 · doi:10.24926/265535.1534

The Perfect Storm of Retirement Insecurity: Fixing the Three-Legged Stool of Social Security, Pensions, and Personal Savings

2007· article· en· W3122900349 on OpenAlexaboutno aff
Stephen F. Befort

Bibliographic record

VenueMinnesota law review · 2007
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Literacy, Pension, Retirement Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsStormBoomBaby boomSocial securityWork (physics)Political scienceEconomicsDemographic economicsSociologyDemographyGeographyMeteorologyEngineeringLaw

Abstract

fetched live from OpenAlex

The Perfect Storm. 1 He tells the story of a fishing boat based out of Gloucester, Massachusetts, that was lost at sea near Newfoundland during a 1991 Halloween nor'easter. 2 The Perfect Storm chronicles the unique mix of meteorological forces that coalesced to create a once-in-a-century maelstrom of devastating proportions. 3 Another potential perfect storm threatens the financial well being of future retirees in the United States.As with the Halloween nor'easter, this storm has the potential to cause exceptional damage due to the confluence of a unique set of circumstances.This time, however, the forces at work are actuarial rather than meteorological.Two factors are principally responsible for the looming retirement insecurity storm.First, the coming generation of retirees will be large and long-lived.The retirement of the baby boom generation will create a retiree cohort historically unequaled in size.Continued increases in life expectancies prompt many analysts to anticipate that this cohort will have a retire-

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)0.000
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 score0.768
Threshold uncertainty score0.716

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.016
GPT teacher head0.249
Teacher spread0.232 · 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

Citations2
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueMinnesota law reviewSame topicFinancial Literacy, Pension, Retirement AnalysisFrench-language works237,207