Operationalizing governability: a case study of a Lake Malawi fishery
Bibliographic record
Abstract
Abstract Governability is seen as an adjustment process between governing needs and governing capacities. Understanding these two aspects and the interplay between them in a governance setting would pave a way for managing the pervasive difficulties confronting fisheries. In this study, we demonstrate how to operationalize the concept of governability by applying governability assessment framework to an inland fishery in the Southeast Arm of Lake Malawi. First, the needs and the demands of the natural and socio‐economic aspects of the lake fishery system are examined according to four properties – diversity, complexity, dynamics and scale. Similarly, the capacities of the governing system are assessed. The characteristics of the governing interactions between these systems are next explored to provide a basis for improving governability. Assessment findings produce a systematic and holistic image of the fishery, and offer some insights into key governance issues and processes. In the Southeast Arm fishery, these include taking a close look at the internal, normative drivers of illegal fishing to ease the socio‐economic complexity and streamlining the institutional structure to boost governing capacity.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".