Growing hope: Island agriculture and refusing catastrophe in climate change adaptation in Fiji
Bibliographic record
Abstract
In both media and policy, climate change is broadly framed as the promise of catastrophe for small island states such as Fiji. This framing is often used to attract adaptation investment in islands, the targets and directives of which are frequently market-based and oriented toward economic-growth development models. In Fiji, this takes the form of land tenure policy and efforts to attract investment to support agricultural modernization. Such a pattern is the source of scholarly and activist critique that climate change adaptation is nothing more than a repackaging of neoliberal development. This paper seeks to situate such critique alongside parallel attention to climate change adaptation practices emerging from alternative, hopeful frames and aimed at less national development driven efforts. In doing so, it centers adaptation as a space of unsettled struggle and asks, in what ways do climate change adaptation practices in Fiji align and conflict with dominant framing of island vulnerability and climate catastrophe, and how might they suggest alternative adaptive interventions that renegotiate these frames? Specifically, this paper focuses on efforts to promote ‘traditional’ agriculture throughout Fiji as an endogenous and hopeful form of adaptation, and one consistently opposed to efforts at agricultural modernization as an adaptation strategy.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.025 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| 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".