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Record W7131921475

The Effects of Measures to Encourage Voluntary Pension Savings in the Republic of Croatia

2024· dissertation· hr· W7131921475 on OpenAlexaboutno aff
Iva Tončić

Bibliographic record

VenueRepository of the University of Rijeka, Faculty of Economics and Business · 2024
Typedissertation
Languagehr
FieldBusiness, Management and Accounting
TopicFinancial Literacy, Pension, Retirement Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsCroatianThe RepublicPensionQuarter (Canadian coin)
DOInot available

Abstract

fetched live from OpenAlex

Ovaj završni rad istražuje učinke mjera poticanja dobrovoljne mirovinske štednje u Republici Hrvatskoj s posebnim naglaskom na analizi mirovinskog sustava. Definiraju se pojam i struktura mirovinskog sustava, uz naglasak na prednosti i rizike ulaganja u mirovinske fondove koji su podijeljeni na otvorene i zatvorene te obuhvaćaju tri mirovinska stupa. Prvi stup, koji je obavezan, temelji se na načelu međugeneracijske solidarnosti i osigurava mirovine sadašnjim umirovljenicima. Drugi stup, također obavezan, omogućuje povoljnije mogućnosti mirovine putem privatnih mirovinskih društava pod nadzorom Hanfe. Treći stup predstavlja dobrovoljnu mirovinsku štednju koja dopunjuje obavezne mirovinske sustave. Rad se posebno fokusira na analizu dobrovoljnih mirovinskih fondova, uključujući karakteristike fondova, strategije ulaganja, troškove upravljanja i rizike. Naglašava se važnost trećeg mirovinskog stupa za dugoročnu stabilnost mirovinskog sustava, uz razmatranje faktora visine mirovine i opcija isplate. Cilj rada je pružiti detaljan uvid u funkcioniranje mirovinskog sustava i identificirati mjere koje bi mogle unaprijediti dobrovoljnu mirovinsku štednju, osiguravajući time bolju financijsku stabilnost i sigurnost za buduće umirovljenike.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.232
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.008
GPT teacher head0.189
Teacher spread0.182 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

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