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Record W2516235534 · doi:10.20956/ijas.v4i1.248

Building Bridges to an Uncertain Future Lived Now: Lessons from the Use of Participatory Action Research and Theory of Change Towards A Realistic Community-Based Participatory Monitoring and Evaluation System

2016· article· en· W2516235534 on OpenAlexaff
Enrique M. Avila, Lutgarda L. Tolentino, Claudia B. Binondo, Maripaz Perez, Marina Apgar

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2016
Typearticle
Languageen
FieldDecision Sciences
TopicComplex Systems and Decision Making
Canadian institutionsInstitute on Governance
Fundersnot available
KeywordsParticipatory action researchCitizen journalismVisionMonitoring and evaluationParticipatory evaluationProcess managementProcess (computing)Action researchSet (abstract data type)Product (mathematics)AgricultureAction (physics)Theory of changeSociologyEnvironmental resource managementEnvironmental planningPublic administrationPolitical scienceComputer scienceEconomic growthEngineeringGeographyEconomicsLawPedagogyMathematics

Abstract

fetched live from OpenAlex

Building on experience from the CGIAR Research Program on Aquatic Agricultural Systems implemented by WorldFish in the Visayas and Mindanao regions of the Philippines, known as the VisMin Hub, we describe the development and evolution of a monitoring and evaluation (M&E) system emerging from the facilitated action-reflection cycles of testing and adopting theories of change carried out with community partners through participatory action research (PAR). The former guides our community partners and us, as members of the potentially emergent PAR groups, towards the realization of the community’s vision; the latter facilitates learning to understand what, how and why change is unfolding. Unlike the conventional M&E system where indicators are pre-set at the beginning of program implementation, these processes result in an organically-evolved, communitybased participatory M&E system that is continuously revised according to contexts to guide communities towards realizing their visions. Its ultimate outcome is enhanced people’s capacity to own the product and process, giving rise to an internally-driven change. Towards the end, the paper offers an iterative discussion of learnings from implementing such an approach.

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)0.037
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.411
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0370.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0020.001
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 head0.957
GPT teacher head0.718
Teacher spread0.239 · 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.

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

Citations1
Published2016
Admission routes1
Has abstractyes

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