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Record W2998259328 · doi:10.1017/s0032247419000615

Where did all the men go? The changing sex composition of the Russian North in the post-Soviet period, 1989–2010

2019· article· en· W2998259328 on OpenAlexaboutno aff
Timothy Heleniak

Bibliographic record

VenuePolar Record · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicDemographic Trends and Gender Preferences
Canadian institutionsnot available
Fundersnot available
KeywordsLife expectancyDemographySoviet unionSex ratioPopulationQuarter (Canadian coin)GeographyPeriod (music)Political scienceSociologyPolitics

Abstract

fetched live from OpenAlex

Abstract Like the northern periphery regions of other Arctic countries, the Russian North had a higher male–female sex ratio than the rest of the country. During the two decades following the breakup of the Soviet Union, the male sex ratio in the Russian North declined considerably, from 101 males per 100 females in 1989 to 92 in 2010. The regions and population of the Russian North were greatly impacted by the shift in northern development approaches from the centrally planned system of the Soviet Union to the market-oriented system of Russia. This paper examines the decline in the male population in the Russian North based on data from the 1989, 2002 and 2010 population censuses. The paper finds that only one quarter of the decline in the male sex ratio in the Russian North can be attributed to higher male outmigration and that three quarters are the result of significantly higher and widening gaps between females and males in life expectancy. The conclusion is that men in the Russian North coped with the social and economic upheavals by dying prematurely not by migrating. The leading causes of death for men were cardiovascular diseases and external causes such as murder, suicide and accidents.

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 score0.177
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.019
GPT teacher head0.262
Teacher spread0.243 · 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

Citations8
Published2019
Admission routes1
Has abstractyes

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