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Record W2171263761

Diagnóstico rural participativo de dos comunidades del municipio La Cañada de Urdaneta, estado Zulia

2004· article· es· W2171263761 on OpenAlexaboutno aff
Maritzabel Materán, Fátima Urdaneta, E Martínez, J Castillo, N. Rincón

Bibliographic record

Venuenot available
Typearticle
Languagees
FieldAgricultural and Biological Sciences
TopicAgricultural and Food Production Studies
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPolitical scienceRural populationPopulationGeographyRural areaSociologyDemographyPhilosophy
DOInot available

Abstract

fetched live from OpenAlex

El objetivo de esta investigacion consistio en diagnosticar participativamente dos comunidades del municipio La Canada de Urdaneta del estado Zulia, como base para el Plan de Desarrollo Rural Integral Sustentable (DRIS) de La Corporacion para el desarrollo de la region Zuliana (CORPOZULIA). La Investigacion conto con elementos de la investigacion Participativa, investigacion accion y no experimental-descriptiva. La poblacion considerada estuvo conformada por 416 productores, localizados en las comunidades «La Chinita» y «Los Bienes» (Bosque muy seco tropical). Se realizaron talleres, para incentivar la participacion, caracterizar las comunidades e identificar los problemas, ademas, se aplico una encuesta sociotecnico- economico a una muestra (57%). Los resultados evidencian que las comunidades La Chinita y Los Bienes poseen una poblacion joven, cuyo promedio es 28 y 29 anos respectivamente, la agricultura representa el 41% de la actividad economica en la comunidad La Chinita y 31% en Los Bienes. Los hogares se encuentran en situacion de pobreza, ademas los principales problemas son la falta de agua, electricidad, vialidad, educacion y salud, a estos se les suma la inseguridad personal y falta de titulos propiedad de la tierra. Se recomienda disenar una propuesta de desarrollo rural para ambas comunidades en los aspectos: social, economico y juridico-institucional.

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)0.000
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 score0.366
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.022
GPT teacher head0.252
Teacher spread0.231 · 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
Published2004
Admission routes1
Has abstractyes

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