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Record W2561628523 · doi:10.4000/emscat.2820

Were the historical Oirats “Western Mongols”? An examination of their uniqueness in relation to the Mongols

2016· article· fr· W2561628523 on OpenAlexaff

Bibliographic record

VenueÉtudes mongoles sibériennes centrasiatiques et tibétaines · 2016
Typearticle
Languagefr
FieldEnvironmental Science
TopicRangeland Management and Livestock Ecology
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsHumanitiesHistoryEthnologyPhilosophyArt

Abstract

fetched live from OpenAlex

Cet article examine la singularité des Oïrats par rapport aux Mongols pour reconsidérer la pratique consistant à les désigner comme des “Mongols occidentaux”. Les Oïrats, qui ne faisaient pas partie des Mongols originels menés par Gengis Khan, sont devenus, après l’éclatement de l’empire mongol, les Dörben Oïrat, une confédération nomade principalement composée de groupes non-mongols. Les tests ADN portant sur le chromosome Y des Kalmouks et des Mongols modernes montrent que ceux-ci ont des origines hétérogènes. Les Oïrats, bien qu’ils se considèrent comme un peuple mongolique, considéraient les Mongols comme une entité distincte. De même, les Mongols voyaient les Oïrats comme des ennemis étrangers (qari daysun). Les histoires chinggiside et timouride d’Asie centrale font également la différence entre les deux. Par conséquent, je suggère que les historiens reconnaissent les Oïrats comme un peuple distinct, comme les Xiongnu, les Xianbei, les Kök Turcs, les Ouïghours et les Kirghizes.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.005
Scholarly communication0.0030.002
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.246
Teacher spread0.227 · 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 designNot applicable
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

Citations7
Published2016
Admission routes1
Has abstractyes

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