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Record W2781785914 · doi:10.4000/nuevomundo.71668

Relations interethniques et métissages dans les aldeias indigènes de Rio de Janeiro : classifications ethniques et luttes politiques (XVIIIe et XIXe siècles)

2017· article· fr· W2781785914 on OpenAlexaboutno aff
Maria Regina Celestino de Almeida

Bibliographic record

VenueNuevo mundo mundos nuevos · 2017
Typearticle
Languagefr
FieldSocial Sciences
TopicHistory of Colonial Brazil
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousEthnologyHumanitiesPolitical scienceHistoryArt

Abstract

fetched live from OpenAlex

À l´époque de la colonisation portugaise, plusieurs peuples indigènes ont été déplacés de leurs terres d´origine et installés dans des villages (aldeias) coloniaux dans les environs de Rio de Janeiro. Ces Indiens y ont reconstruit leurs identités et, au début du XIXème siècle, ils s´identifiaient – et étaient identifiés – tantôt comme Indiens, tantôt comme métis. Cet article discute les relations interethniques entre ces Indiens et les non-Indiens, en mettant l’accent sur les controverses concernant les classifications ethniques perçues comme des outils de lutte politique. Les documents concernant les conflits fonciers dans les aldeias montrent que des alliances étaient scéllées entre Indiens et non-Indiens mais que celles-ci variaient d´un conflit à l´autre, tout comme les classifications ethniques, dans la mesure où certains Indiens étaient parfois considérés comme des non-Indiens, sans pour autant abdiquer de leur identité d’Indien ou cesser d’être reconnus en tant que tel. La distinction rigide entre les uns et les autres avait pourtant un poids politique considérable, qui apparaît clairement dans la documentation. Sans nier les conflits d´intérêts entre Indiens et colons, surtout concernant la propriété de la terre, il faut néanmoins les nuancer et étudier de manière approfondie le profil des acteurs impliqués.

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)0.003
metaresearch head score (Gemma)0.004
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 score0.045
Threshold uncertainty score0.089

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0070.014
Scholarly communication0.0050.002
Open science0.0010.004
Research integrity0.0010.002
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 head0.106
GPT teacher head0.424
Teacher spread0.318 · 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".

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Citations0
Published2017
Admission routes1
Has abstractyes

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