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Record W2330158408 · doi:10.2166/wp.2010.089

Implementing participatory irrigation management in Thailand

2010· article· en· W2330158408 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueWater Policy · 2010
Typearticle
Languageen
FieldEngineering
TopicWater resources management and optimization
Canadian institutionsUniversity of Manitoba
FundersInstitute for Humanities Research, Arizona State UniversityAgricultural Research Development AgencyU.S. Department of Agriculture
KeywordsIrrigation managementIrrigationGovernment (linguistics)BusinessCitizen journalismCorporate governanceParticipatory managementEnvironmental planningEnvironmental resource managementPolitical scienceManagementEconomicsGeographyFinance

Abstract

fetched live from OpenAlex

Participatory Irrigation Management (PIM) was formally adopted in Thailand in 2004. The involvement of farmers in water management decision making is necessary to meet the implementation challenges of this initiative. As such, the research presented in this paper considered the level of farmer involvement in water management and decision making, and the lessons learned by both government officials and farmers through the implementation of PIM in Thailand to date. Data collected from document reviews and a total of 44 semistructured face-to-face and telephone interviews of public irrigation officials and farmers nationwide show that farmers possess the full potential to manage irrigation water by themselves, and that they are making important changes to governance systems for irrigation. However, they need both the opportunity and the continuing supportof public irrigation officials for success, which is currently only being partly achieved through the PIM initiative.

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.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.540
Threshold uncertainty score0.285

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.013
GPT teacher head0.242
Teacher spread0.229 · 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