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Record W2058926397 · doi:10.1080/10599240902751062

Collaboration on Contentious Issues: Research Partnerships for Gender Equity in Nicaragua's Fair Trade Coffee Cooperatives

2009· article· en· W2058926397 on OpenAlexaff
Lori Hanson, Vincent Terstappen

Bibliographic record

VenueJournal of Agromedicine · 2009
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMicrofinance and Financial Inclusion
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsGeneral partnershipEmpowermentParticipatory action researchEquity (law)Public relationsCitizen journalismCommunity-based participatory researchPolitical scienceEconomic growthBusinessEconomicsFinance

Abstract

fetched live from OpenAlex

In recent years, the use of collaborative and partnership approaches in health and agricultural research has flourished. Such approaches are frequently adopted to ensure more successful research uptake and to contribute to community empowerment through participatory research practices. At the same time that interest in research partnerships has been growing, publications on methods, models, and guidelines for building these partnerships have proliferated. However, partnership development is not necessarily as straightforward or linear a process as such literature makes it appear, particularly when the research involves divisive or contentious issues. This paper explores prevailing views on research partnerships, and also questions the applicability of partnership models using an emerging research program around gender equity and health in Fair Trade coffee cooperatives in Nicaragua as an example. Moreover, the paper introduces some of the complicated issues facing the authors as they attempt to develop and expand partnerships in this research area. The paper culminates with a series of strategies that the authors plan to use that offer alternative ways of thinking about building research partnerships concerning controversial or complex issues in the field of community health and development.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.444
Threshold uncertainty score0.474

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
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 head0.359
GPT teacher head0.447
Teacher spread0.089 · 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 designTheoretical or conceptual
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

Citations6
Published2009
Admission routes1
Has abstractyes

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