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Dynamique identitaire des habitants de la Paz (PACEÑOS), les Aymaras et les Metis.

2019· dissertation· W7148969226 on OpenAlexaboutno aff
Estrella Rivero Herrera

Bibliographic record

Venuenot available
Typedissertation
Language
FieldSocial Sciences
TopicIndigenous Cultures and History
Canadian institutionsnot available
Fundersnot available
KeywordsUrban povertyUrban environmentUrban policyUrban planning

Abstract

fetched live from OpenAlex

Dans la ville de La Paz il y a deux types d`identité, que les gens s`attribuent à soi même, l`aymara et le métis. Il sont les résultat de facteros socio-economiqués et culturels. En général, lá attribution de l`identité metisse concerne a les classe moyenne urbaine et l`identité aymara concerne a les classe indigène aymara. Cependant Rossana Barragán (2009) consider que certaines personnes vivant dans les zones rurales s`identifient aussi métisse et il ya des gens qui vivent dans les zones urbaines qui s`identifient antan etat aymara. Afin d`approfondir cette étude, que nous considérons l`espace géographique génère des identitès, nous proposons une trosième catégorie de l`analyse, de l`identité paceniènne, l`auto-identification comme une possibilité, et si elle va à un projet politique. Dans ce contexte, il est important d`etudier l`identité aymara, metises et paceniènne et en tenant compte des différentes étapes de la migration, en tenant compte de des catégories de sexe, le statut d`emploi et niveau d'activité. Ainsi, il est nécessaire de se demander l´activité du travail a connu l'identité de reconstruction paceniènne, aymara, mixte et si cela commence à émerger une identité de paceniènne, il est important de savoir comment ces identités sont positionnés dans la société urbaine de La Paz.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.071
Threshold uncertainty score0.140

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0030.001
Scholarly communication0.0020.002
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.014
GPT teacher head0.331
Teacher spread0.317 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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