Time and Timing: Exploring Farmers’ Reluctance to Use Web-based Discussion Groups
Bibliographic record
Abstract
Web-based discussion groups (WBDG) are a promising method for engaging in research with farmers. Their most attractive features are that they are expected to encourage a rich dialogue, as successive posters built on the contributions of the original visitors, as well as allow all opinions to be expressed and time for issues to be thought through. The research for this paper uses a failed WBDG (designed to explore the management intentions of a group of irrigation farmers facing significant water reforms) as a case study. Informal unstructured telephone interviews were carried out with twenty-eight WBDG non-participants to identify and explore best practices in WBDG development and identify possible barriers to participation. After the analysis of interview responses a number of issues were identified as contributing to decisions regarding participation in the WBDG including: (1) lack of time, (2) uncertain benefits/relevance, (3) lack of IT skills and abilities and (4) Internet speed. Although lack of time was the most commonly cited reason, it is more likely that an individual's willingness to participate was related to perceptions of relevance either for that individual or for their community. Relevance is also influenced by timing, in that change caused by exogenous forces is continually happening, so that depending on the nature of the changes, at any point in time there may be greater or lesser relevance. As farmers appear to use the Internet sparingly, reluctantly and in a way that best suits their needs, the extent to which they choose to participate in WBDGs will be strongly dependent on the farmers themselves, but will also be affected by the relevance of the issue being considered, the timing of the research and how this interacts with and influences the relevance of the discussion topic.
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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 | 0.017 | 0.126 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
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".