Building cultural capital in drought adaptation: lessons from discourse analysis
Bibliographic record
Abstract
As governments and primary industries work to build the climate resilience of Australian agriculture, individual producers are often called upon to implement strategies to become more adaptive in the face of drought. These strategies include infrastructural changes to agricultural businesses, changes to practices, and the adoption of new skills and knowledge. The transition towards greater drought adaptiveness will also demand broader cultural shifts in the way that drought is defined and approached as an issue facing primary producers. This paper presents the results of a discourse analysis conducted as part of social research exploring the cultural barriers to drought preparedness within the Queensland Government’s Drought and Climate Adaptation Program (DCAP). Focusing on media and government accounts, the analysis found two different ways of framing drought and its management in Queensland agriculture. The first, which is dominant in media accounts, emphasises the disruptive power of drought, presenting it as a profound difficulty for producers that is managed using endurance, hope and ingenuity. This frame adopts highly evocative discursive strategies oriented towards mobilising community sentiment and support for producers. The second, which is less prominent overall, downplays drought’s disruptive power and counters the emotionality of the adversity discourse by presenting drought as a neutral business risk that can be managed using rational planning skills and scientific knowledge. In discussing these two frames, this paper suggests strategies whereby drought adaptation frames might be made more powerful using more meaningful and emotive narratives that showcase it as a vital practice for ensuring agricultural livelihoods and rural futures in a changing climate.
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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 | 0.023 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.015 | 0.034 |
| Scholarly communication | 0.016 | 0.024 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".