Cultivating Community: A Discursive Study of Environmental Water Policy in Farming Communities in the Murray-Darling Basin
Bibliographic record
Abstract
This dissertation examines the politicized discourses of water management in New South Wales (NSW), Australia.The case of the Murray-Darling Basin (MDB) offers insight into how dominant discourses can serve to silence meaningful perspectives and alternative solutions to a complex and weighty environmental crisis.The case also shows how marginalized actors push back, resisting both from inside and outside dominant discursive frames and assumptions.A discursive construction of nature that effectively excludes people from its ambit, coupled with a long history of top-down, expert-driven water management policy in the MDB, has had devastating effects on farming communities attempting to deal with the twin impacts of drought and associated policy reforms.The research shows how key elements of three environmental problem-solving discourses identified by John Dryzek, namely administrative rationalism, economic rationalism, and democratic pragmatism, all helped shape the environmental policy landscape in the MDB.This dissertation then examines the effects of a discourse I term 'green environmentalism' on farming communities.Finally, I construct an alternative discourse that helps to explain how farmers understand the challenges their communities are facing.This discourse of resistance-community-centrism-seeks to put human social relationships at the heart of environmental decision-making.Community-centrism provides a much-needed positive reconceptualization of environmental problem-solving in the MDB, surfacing economic, environmental, and social opportunities in ways missed by the four dominant discourses.The research demonstrates how critical reflection on policy discourses helps us envisage an alternative future that can provide for the needs of the economy, society, and the environment.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.030 | 0.033 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".