Local Challenges and Successes Associated with Transitioning to Sustainable Food System Practices for a West Australian Context: Multi-Sector Stakeholder Perceptions
Bibliographic record
Abstract
Large-scale food system practices have diminished soil and water quality and negatively impacted climate change. Yet, numerous opportunities exist to harness food system practices that will ensure better outcomes for human health and ecosystems. The objective of this study was to consider food Production, Processing, Access and Consumption domains, and for each determine the challenges and successes associated with progressing towards a sustainable food system. A workshop engaging 122 participants including producers, consultants, consumers, educators, funders, scientists, media, government and industry representatives, was conducted in Perth, Western Australia. A thematic analysis of statements (Successes (n = 170) or Challenges (n = 360)) captured, revealed issues of scale, knowledge and education, economics, consumerism, big food, environmental/sustainability, communication, policies and legislation, and technology and innovations. Policy recommendations included greater investment into research in sustainable agriculture (particularly the evidentiary basis for regenerative agriculture), land preservation, and supporting farmers to overcome high infrastructure costs and absorb labour costs. Policy, practice and research recommendations included focusing on an integrated food systems approach with multiple goals, food system actors working collaboratively to reduce challenges and undertaking more research to further the regenerative agriculture evidence.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".