MétaCan
Menu
Back to cohort
Record W2553158549

DEMOGRAPHIC WINDOW IN THE CZECH REPUBLIC (WITH INCREASING RETIREMENT AGE)

2015· article· en· W2553158549 on OpenAlexaboutno aff
Jitka Langhamrová

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicInsurance, Mortality, Demography, Risk Management
Canadian institutionsnot available
Fundersnot available
KeywordsCzechPopulation projectionPopulationDemographic dividendAge structureDemographyFertilityPopulation ageingDemographic changePeriod (music)Retirement ageQuarter (Canadian coin)Demographic economicsGeographyEconomicsPensionSociology
DOInot available

Abstract

fetched live from OpenAlex

The demographic window (or demographic dividend, gift, opportunity etc.) is a period when proportion of the non-productive parts of the population is low owing to an already low fertility and a still high mortality so the productive part of the population outweights the nonproductive part. This period is temporary and terminates when people of productive age get older, and due to mortality decline the proportion of the post-productive economic generation relatively rapidly increases. The paper presents the development of proportion of the population in pre-productive, productive and post-productive age since 1950 by present time and the projection until 2100 according to the medium variant of the population projection of the Czech Republic published by the Czech Statistical Office in 2013 and the occurrence of the demographic window. Lengthening the time of the study and the permanent increase of retirement age are taken into account. The permanent increase of retirement age considerably prolongs the period of duration of the demographic window.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.019
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.059
GPT teacher head0.314
Teacher spread0.256 · 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 source (direct Gemma or distilled Codex), 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
Published2015
Admission routes1
Has abstractyes

Explore more

Same topicInsurance, Mortality, Demography, Risk ManagementFrench-language works237,207