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Record W2148449788 · doi:10.4337/9781781951040.00020

Whose Money is it Anyhow? Governance and Social Investment in Collective Investment Funds

2004· book-chapter· en· W2148449788 on OpenAlexaboutno aff
R. Kent Weaver

Bibliographic record

VenueEdward Elgar Publishing eBooks · 2004
Typebook-chapter
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Literacy, Pension, Retirement Analysis
Canadian institutionsnot available
FundersStrongCenter for Retirement Research, Boston CollegeAustralian GovernmentBoston CollegeU.S. Social Security Administration
KeywordsPensionIncentiveRestructuringPrivate pensionCorporate governanceInvestment (military)BusinessEconomicsPopulationGlobal assets under managementEconomic policyLabour economicsInstitutional investorFinanceMarket economyPolitical science

Abstract

fetched live from OpenAlex

Over the past two decades, an aging population and budgetary stress have led to substantial changes in pension systems throughout the world. Many countries initially responded to pension funding crises with incremental reforms. A number of countries have also engaged in a more fundamental restructuring of their pension systems. Several other countries have also made changes in their defined benefit pensions. Finally, some countries have changed the governance of tax-privileged pension savings to provide increased incentives for private retirement savings, despite very mixed evidence about whether such incentives are effective in increasing overall savings rates. These seemingly disparate responses to the pension funding crisis in fact raise a common set of issues about the in governance of such funds. Should their purpose be solely to maximize returns for their (individual or collective) beneficiaries, or should they serve public ends as well? This paper examines how several OECD countries have addressed the public/private divide in collective investment buffer funds, drawing on the experience of Canada, New Zealand and Sweden, as well as the Swedish experience with a default (for those who do not make an active fund choice) in the individual account defined contribution tier of its system.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.571
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0030.003
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.026
GPT teacher head0.222
Teacher spread0.196 · 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 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

Citations10
Published2004
Admission routes1
Has abstractyes

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