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Record W2780663772 · doi:10.25336/p6m912

1939–1945: Une démographie dans la tourmente

2017· article· fr· W2780663772 on OpenAlexaffvenue
Roderic Beaujot

Bibliographic record

VenueCanadian Studies in Population · 2017
Typearticle
Languagefr
FieldArts and Humanities
TopicFrench Historical and Cultural Studies
Canadian institutionsWestern University
Fundersnot available
KeywordsGeography

Abstract

fetched live from OpenAlex

Following an analogous publication on World War I (Rohrbasser 2014), this collection is unique for its inclusion of articles written both shortly after the war and over the next half-century.It also showcases some of the early work done at France's Institut National d'Etudes Démographiques (INED), itself founded in 1945.With a preface by Henry Rousso, the edited collection consists of eighteen chapters of previously published articles.Ten of the chapters were published in the first three volumes of INED's Population in 1946-48, and two others were in the 1988 and 1995 volumes.The six remaining chapters were published in other places between 1947 and 2005.The four sections in the collection treat: (i) war losses, (ii) nuptiality and births, (iii) childhood, and (iv) deportations, exterminations, and displaced persons.The demographic counts are mind-boggling: victims of combat, civilian losses, extermination of targeted populations, deportations, and displaced persons.Rousso gives the total deaths at 38-40 million in Europe and 55-62 million in the world.This is about four times the number of deaths in World War I.The total associated population movements, refugees, displacements, and forced migrations up to 1951 amounts to 40 million people.The authors observe that 40 million was the size of the population of France at the time of the Second World War.While the focus is on the population counts, the demographic turmoil ('une démographie dans la tourmente') had broad geopolitical consequences, of interest to historians and political scientists.The deliberate decimation of certain populations, eviction of specific minorities from areas where they had lived for generations, and associated refugee movements and resettlements caused the uprooting of long-established relations across ethnicity and class.Some authors speak of Europe as a whole becoming a continent of refugees, while others speak of the disappearance of minorities as postwar states became more ethnically homogeneous.By country, the total losses were by far the largest in the former USSR, estimated by Alain Blum and Sergej Maksudov at 26 million (not counting the 0.6 million persons who left the USSR; p. 102).In an estimate published in 1947 by Paul Vincent, the figure of 17 million had been used for the USSR (p.26), but this did not include the victims of the Stalinist regime itself, during the war and in the aftermath of the upheavals caused by the war.The Blum and Maksudov chapter is based on a text by Sergej Maksudov that was smuggled out of the Soviet Union and published in Cahiers du monde russe et soviétique (Maksudov 1977).The estimate of 26 million includes 10.6 million military losses (9.7 million Red Army deaths in combat, in hospital, or in captivity, plus 900,000 deaths of Soviet Partisans and civilians engaged in militias), 8.3 million civilian losses (1.0 million killed in combat, 0.9 million died in the siege of Leningrad, 2.7 million Jews exterminated by the Nazi regime, and 3.8 million in excess mortality caused by German occupation), and 7.0 million losses in territories not under German occupation (1.6 million as prisoners of the gulag or as higher mortality in populations relocated by Stalin, as well as 200,000 soldiers executed by Stalin, 300,000 losses due to conflicts between the Red Army and insurrectionist movements in territories annexed in 1939-40, 1.0 million in the 1946 famine, and 3.9 million in excess mortality of the civilian population in territories not under German occupation; p. 102).

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.256
Threshold uncertainty score0.516

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.007
Science and technology studies0.0040.003
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0100.001

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 head0.060
GPT teacher head0.274
Teacher spread0.214 · 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".

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Citations0
Published2017
Admission routes2
Has abstractyes

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Same venueCanadian Studies in PopulationSame topicFrench Historical and Cultural StudiesFrench-language works237,207