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Record W2940545014 · doi:10.31857/s0869-5873893221-231

Demographic implications of social deviations of Russian youth

2019· article· en· W2940545014 on OpenAlexaboutno aff
Sergey Ryazantsev, V. G. Semenova, Alla Ivanova, T.P. Sabgayda, Galina N. Evdokushkina

Bibliographic record

VenueВестник Российской академии наук · 2019
Typearticle
Languageen
FieldMedicine
TopicHealthcare Systems and Public Health
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)European unionDemographyPolitical scienceDemographic economicsDevelopment economicsGeographySociologyEconomicsEconomic policy

Abstract

fetched live from OpenAlex

The article provides an assessment of the demographic losses among Russian youth due to social deviations – suicides, murders, alcohol and drug poisoning compared with the countries of the “old” (before May 2004) and the “new” (after May 2004) European Union. It has been shown that in Russia and in Europe over the past 30 years, the contribution of losses due to deviant behavior to the total mortality of young people has increased, but in Russia this undoubtedly preventable factor has been of special significance. Currently, this factor causes more than a third of the total mortality of young men in our country and almost a quarter of their contemporaries. The evolution of the structure of losses caused by social deviations testifies to multidirectional processes in Russia and Europe. If both in the "old" and in the "new" European Union the importance of suicides increases, in Russia there is damage with uncertain intentions. In essence, due to this vague diagnosis, underreporting of deaths from alcohol and drug poisoning, suicides and murders is masked – in general, from one third to 100% of cases. This means that the death rate from social deviations in Russia compared to the EU is even more than official statistics show.

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 score0.269
Threshold uncertainty score0.695

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 science0.0000.000
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 head0.039
GPT teacher head0.345
Teacher spread0.306 · 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
Published2019
Admission routes1
Has abstractyes

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