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Record W1865057554 · doi:10.7202/1026171ar

Colonisation, acculturation et métissage

2014· article· fr· W1865057554 on OpenAlexaffvenue
Mikhail Bashkirov

Bibliographic record

VenueAnthropologie et Sociétés · 2014
Typearticle
Languagefr
FieldSocial Sciences
TopicArctic and Russian Policy Studies
Canadian institutionsInstitut d'Histoire de l'Amérique Française
Fundersnot available
KeywordsHumanitiesAcculturationPopulationEthnologyImmigrationArtPolitical scienceSociologyDemography

Abstract

fetched live from OpenAlex

Cet article présente l’histoire et l’émergence des communautés métissées de la Iakoutie du XVIIe au XIXe siècle. Dès l’époque des premiers contacts au XVIIe, les Iakoutes, une population autochtone de la Sibérie, ont subi l’influence économique, sociale et politique des colonisateurs russes. Le métissage entre les Russes et les populations locales a permis l’émergence de communautés métissées dont la culture est distincte de leurs ascendants russes et autochtones. Le processus d’adaptation aux différentes conditions géographiques et naturelles des groupes d’immigrants russes a défini la ligne de développement de ces communautés en Iakoutie. Chaque communauté avait un caractère unique et une identité locale. L’histoire des villages de Amga-Sloboda, Russkoye Ustye et Pokhodsk peut illustrer cette tendance. En général, la population de ces villages porte le nom de russkie starozhilu (« vieux colons russes »), mais en même temps chacune de ces communautés a transformé des éléments de sa culture russe et iakoute et se distingue fortement des autres communautés voisines.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.240
GPT teacher head0.584
Teacher spread0.344 · 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 designQualitative
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

Citations1
Published2014
Admission routes2
Has abstractyes

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