Smallholder sugarcane growers, indigenous technical knowledge, and the sugar industry crisis in Fiji
Bibliographic record
Abstract
This is a cross-disciplinary study that draws upon the agronomic, ecological, and social sciences to analyse the current crisis facing the sugar industry in Fiji. Its particular focus is the livelihood crisis facing the smallholder sugarcane growers, and it explores the potential of their local and traditional farming knowledge as a source of solutions for both crises. It argues, however, that present proposals for reforming the sugar industry in Fiji are wedded to the industrial agricultural paradigm and a globalized corporate food regime that is the source of the problems it currently faces and which threatens the future of the smallholder sugarcane farming system along with its local traditional knowledge. The thesis draws inspiration from Agroecology as an agricultural paradigm alternative to the conventional industrial paradigm to advocate for greater attention to be given to smallholder sugarcane growers and their local and traditional farming knowledge in seeking solutions to the crisis of the sugar industry in Fiji. To explore these complex issues, the thesis adopts a cross-disciplinary, mixed-method approach. Participant observation, focus group discussions, and informal interviews with smallholder sugarcane farmers were used to elicit their views, feeling, thoughts and opinions on the Fiji sugar industry, their relationships with other sugar industry actors, and their own indigenous technical knowledge. Livelihood survey methods and agroecosystem analysis were used to gather quantitative data on household and farm status. This information was analysed using IBM® Social Science software: Statistical Package for Social Sciences (SPSS) and Microsoft® Office Excel Spreadsheet, to provide an up-to-date profile of livelihood and farming situation of smallholder sugarcane growers. Semi-structured interviews with industry stakeholders were used to identify the agricultural problems and socio-economic issues facing the industry and their differing views on the solutions proposed to solve them. Archival material was used to obtain information on past efforts of the sugar industry to develop solutions to problems at the local, national and international levels, and existing academic literature was reviewed for additional information on the contemporary situation of the smallholder sugarcane growers and the Fiji sugar industry as a whole.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".