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Record W7162790729 · doi:10.7251/ap2302069p

MORTALITY TRENDS IN THE REPUBLIC OF SRPSKA IN THE 21ST CENTURY (2001-2022)

2023· article· W7162790729 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueAKADEMSKI PREGLED · 2023
Typearticle
Language
FieldEconomics, Econometrics and Finance
TopicRegional Development and Management Studies
Canadian institutionsnot available
Fundersnot available
KeywordsLife expectancyEmigrationPopulationThe RepublicMortality rateInfant mortalityDeveloped countryDeveloping country

Abstract

fetched live from OpenAlex

Negative demographic trends in Republic of Srpska (negative natural increase, negative migration balance, aging) have an increasingly significant impact on the socio-economic development Republic of Srpska. At the beginning of the 21st century, the long-term decrease in the number of births and increase in the number of deaths were recognized as destabilizing factors of demographic development. The intensification of population emigration to EU countries and other countries of the world (USA; Canada, Australia) is of particular concern. Mortality in Republika Srpska was influenced by various socio-economic, demographic and epidemiological factors. The main goal of this paper is to analyze the changes in mortality indicators within the framework of contemporary trends in population movements in the Republic of Srpska. The results of the research show that certain changes (positive and negative) related to mortality were recorded in the Republic of Srpska (increase in life expectancy at birth, decrease in infant mortality, and some trends that are not favorable, especially those related to the causes of mortality. Although there was until the decrease in the share of deaths from some diseases, a significant increase in the number of deaths from certain diseases (Covid-19) was recorded, which can attributed to an unhealthy lifestyle and various behavioral factors.Key words: Мortality, mortality by cause, life expectancy at birth.

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.

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.455
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.005
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 head0.082
GPT teacher head0.275
Teacher spread0.193 · 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