MétaCan
Menu
Back to cohort
Record W2569173360 · doi:10.5539/res.v9n1p137

The Educational and Social Profile of the Indigenous People of Ecuador: A Multidimensional Analysis

2017· article· en· W2569173360 on OpenAlexvenueno aff
José Manuel Castellano, Efstathios Stefos, Lisa Gaye Williams Goodrich

Bibliographic record

VenueReview of European Studies · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicBusiness, Innovation, and Economy
Canadian institutionsnot available
FundersUniversity of the Aegean
KeywordsDescriptive statisticsIndigenousUnderemploymentUnemploymentFactorial analysisPopulationStatistical analysisMultidimensional analysisPrincipal component analysisTypologyGeographyPsychologySocioeconomicsSociologyDemographyStatisticsMathematicsEconomic growthEconomics

Abstract

fetched live from OpenAlex

The objective of the study is to examine the educational level and the social profile of the indigenous people of Ecuador by means of a descriptive and multidimensional statistical analysis of this sector of the Ecuadorian population, based on data from the National Survey of Employment, Unemployment and Underemployment from 2015. The descriptive analysis shows the frequency and percentages of the variables used in the investigation, while the multidimensional statistical analysis is used in order to show the principal and most important criteria of differentiation and classification among the groups of people investigated. These methods involve a factorial analysis of multiple correspondences which demostrates the criteria of differentiation and a hierarchical cluster analysis to define groups of people according to their common traits.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.054
GPT teacher head0.280
Teacher spread0.226 · 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 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

Citations12
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueReview of European StudiesSame topicBusiness, Innovation, and EconomyFrench-language works237,207