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Estrategias de gobernanza para fortalecer la coordinación interorganizacional y la participación comunitaria en la toma de decisiones de planeación territorial del ecoturismo en el Santuario de Fauna y Flora Los Flamencos, La Guajira – Colombia

2021· dissertation· es· W4287832618 on OpenAlexaff
Ornella Choles Povea

Bibliographic record

Venuenot available
Typedissertation
Languagees
FieldSocial Sciences
TopicSocial Issues and Sustainability
Canadian institutionsImpact
Fundersnot available
KeywordsPolitical scienceHumanitiesGeographyArt

Abstract

fetched live from OpenAlex

En el Santuario de Fauna y Flora Los Flamencos (SFFLF) surgen prácticas de gobernanza para el desarrollo del ecoturismo, que involucran actores como el gobierno local y regional, las comunidades étnicas locales, mayoritariamente indígenas wayúu y afrodescendientes, y otras entidades con competencia en el territorio. A partir de una investigación aplicada (práctica basada en diagnóstico), se identificaron debilidades en las acciones de gobernanza, como la débil articulación de entidades gubernamentales en sus distintos niveles, coordinación deficiente entre los actores para la planeación y manejo del territorio sobre el ecoturismo, ausencia de esquemas efectivos de participación comunitaria en la toma de decisiones sobre estos asuntos, así como un capital social comunitario bajo para la asociatividad y autonomía para desarrollar emprendimientos ecoturísticos. Algunos estudios sobre la gobernanza de ANP abandonan el control estatal para dar paso a una forma autoorganizada. Sin embargo, esta investigación concluye que una única perspectiva de gobernanza resulta insuficiente para abarcar y resolver las situaciones complejas en torno al SFFLF, por lo que apuesta por combinar elementos de formas de gobernanza (comunitaria, multinivel y colaborativa -en red) para construir un marco de gobernanza adaptable a tales complejidades y proponer modelos asociativos de desarrollo local a partir del fomento del ecoturismo.

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)0.003
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 score0.122
Threshold uncertainty score0.243

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0060.004
Scholarly communication0.0050.002
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.012
GPT teacher head0.362
Teacher spread0.350 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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