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Record W3125857326 · doi:10.20472/iac.2016.023.080

THE ROLE OF RESGUARDO LAND ACCESS AND LANGUAGES IN THE INCOME DISPARITY AFFECTING COLOMBIAN INDIGENOUS PEOPLE

2016· article· en· W3125857326 on OpenAlexaff
Rosa Evelia Sánchez García

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Cultures and Socio-Education
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsIndigenousGeographyComputer scienceSocioeconomicsBusinessEconomics

Abstract

fetched live from OpenAlex

This paper addresses the question 'Do land access through resguardos (reserve lands) and ethno linguistic alienation/integration contribute to the relative income poverty of Colombian Indigenous peoples?' To answer this question this investigation regressed the (log of) income unmet basic needs (IUBN) gap between Indigenous and non-minority people on a set of explanatory variables that included: the mean resguardo land size per family (as an indicator of land access) and the percent of Indigenous populations (at the municipal level) that speak Spanish and those that speak their Native language (as indicators of ethnolinguistic alienation/integration). The research results suggest that, for the Andean region, ethnolinguistic integration (speaking a Native language) was in important factor in decreasing the IUBN gap but, for the Amazonian region, ethnolinguistic acculturation (speaking Spanish) increased the IUBN gap. More resguardo land per family, however, tended to be associated with a larger IUBN gap. Further investigated suggested that this was likely due to the fact that resguardos tend to be large where lands are marginal and/or remote, making them economically unproductive.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.475
Threshold uncertainty score0.923

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.007
GPT teacher head0.316
Teacher spread0.309 · 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 teacher head, 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".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

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