The drought, the irrigators, and their photographs: images from the inside
Bibliographic record
Abstract
This paper describes a photo-elicitation study conducted with irrigators in Australia's Murray-Darling Basin during a time of severe drought.This method was chosen because of its ability to elicit the expression of more deeply felt beliefs and values than interviews by themselves.Using visual methods combined with personal interviews, the aim of the study was to uncover the influences on irrigators' decision-making and their expectations for the future.The analysis of the interview transcripts in conjunction with the analysis of the photographs shows that irrigators have become dispirited and often no longer see a future for themselves in the industry.It was also evident that non-profit-maximising values, such as lifestyle and the prospect of family succession, are still powerful influences on irrigators' behaviour, even during a time of such severe financial distress.This study suggests that the programs and policies aimed at assisting irrigators, even during this time when financial issues could have been assumed to have been their most pressing concern, could be better designed so that they provide a more compatible match with irrigators' values and attitudes.This would potentially lead to less social, environmental and economic distress for the individuals and communities of the irrigation regions.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".