Bibliographic record
Abstract
Recent research acknowledges the natural and social dimensions o aquaculture systems. However, studies often create an artificial divide by attempting to address each aspect in isolation. Social science research has tended to overlook the biophysical aspects of aquaculture, while scientific research has uncritically accepted orthodox explanations of environmental outcomes without recognizing the social construction of such systems. This research analyses the case of small-scale tilapia aquaculture in Malawi and presents evidence to support the central argument that aquaculture systems must be addressed through hybrid research. The social dimension of this research reveals that fish farmers in Malawi are rejecting practices which do not work in the local context (fertilization with pond mud) and adopting strategies that do work (irrigation with pond water). The physical component of this research compliments the social by elucidating that irrigation with pond water resulted in higher soil nutrient and moisture content. It is argued that understanding aquaculture impacts on ecosystems may only be achieved through locally-based hybrid research methods that recognize the complex, connected nature of these systems. f
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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 | 0.002 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".