Exploring perspectives of environmental best management practices in Thai agriculture: an application of Q-methodology
Bibliographic record
Abstract
SUMMARY In Thailand, horticultural practices are a significant source of non-point source (NPS) pollution, and the government is considering best management practices (BMPs) as control measures for reducing agricultural NPS pollution to water. A prevailing assumption that farmers’ reactions to regulations will be homogenous is not based on underlying insights into attitudinal positions that may explain alternative behavioural responses. This paper uses Q-methodology to identify attitudinal discourses relating to BMP uptake. The approach combines the strengths of qualitative and quantitative research in order to explore subjectivity. The study is conducted with citrus growers in the Ping river basin, where farmers are facing increasing competition from alternative water uses. Four ‘discourses’ or viewpoints are identified, namely conservationist, traditionalist, disinterested and risk-averse. The different attitudes of these four groups are likely to be associated with distinctive behavioural reactions to the adoption of alternative policy instruments for pollution control. These discourses could usefully inform targeted policies for the control of NPS pollution from agriculture.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.042 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".