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Record W4386475361 · doi:10.3390/jrfm16090396

Measuring the Performance of Private Pension Companies in Türkiye by Gray Relational Analysis Method

2023· article· en· W4386475361 on OpenAlexvenueno aff
Muharrem UMUT

Bibliographic record

VenueJournal of risk and financial management · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Literacy, Pension, Retirement Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsPensionLegislationPension systemPrivate pensionBusinessGray (unit)Private sectorAccountingGovernment (linguistics)Investment (military)Actuarial sciencePublic economicsFinanceEconomicsEconomic growthPolitical sciencePolitics

Abstract

fetched live from OpenAlex

The private pension is a system designed to maintain an income level during passive periods by utilizing the income earned during active working years. It complements the mandatory retirement systems of the public sector and is based on a voluntary participation structure. Additionally, it serves as an investment and savings tool with the ability to provide long-term funds. The legislation for the private pension system was enacted in Türkiye in 2001, and it was implemented in 2003. In addition, a government contribution program was initiated to promote the system in 2013. An automatic enrollment system was introduced in 2017. The effectiveness and performance of individual pension companies play significant roles in the system. This study aims to measure the performance of individual pension companies operating in Türkiye using the gray relational analysis method, which is an effective measurement method, for the years 2016–2022. Subsequently, based on the measurement results, recommendations will be provided.

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)0.003
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.510

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.227
Teacher spread0.211 · 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 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

Citations2
Published2023
Admission routes1
Has abstractyes

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