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Record W7072039417

Towards Critical and Transformative Participatory Development in the Andes

2023· dissertation· en· W7072039417 on OpenAlexaff

Bibliographic record

VenueThe Atrium (University of Guelph) · 2023
Typedissertation
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture, Land Use, Rural Development
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsTransformative learningParticipatory action researchIndigenousGrounded theoryCitizen journalismDecolonizationNarrativeQualitative researchParticipatory developmentAgency (philosophy)
DOInot available

Abstract

fetched live from OpenAlex

The purpose of this thesis is to explore, from an organisational standpoint, what is currently being done to improve participation and capacity development in rural projects within the Andes. The research is based in grounded theory and qualitative participatory methods. It was conducted through semi-structured interviews in Peru from November 1st, 2022, to November 30th, 2022. Two site visits were performed, including a field visit, and a workshop attendance. Results demonstrate a significant emphasis on decolonisation from international non-government organisations, with a lesser importance placed by local NGOs. Local development workers were more concerned about market protections and the over-arching extractive systemic post-colonial structure. Importantly, the researcher acknowledges his own positionality within the colonial structure and has done his best to promote the voices of the local and Indigenous populations. The study seeks to tell experiences of the local and Indigenous peoples, rather than prescribe a narrative or outside perspective.

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)0.096
metaresearch head score (Gemma)0.053
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.096
Threshold uncertainty score0.510

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0960.053
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0210.073
Scholarly communication0.0200.012
Open science0.0030.023
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0030.001

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 head0.037
GPT teacher head0.245
Teacher spread0.207 · 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
Published2023
Admission routes1
Has abstractyes

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