case study from the southern Cape linefishery 1: The difficulty of fishing in a changing world
Bibliographic record
Abstract
Variability on multiple temporal and spatial scales exposes fishers and fishing communities to multiple stressors. The impact and interplay of these stressors need to be considered to improve our understanding of social-ecological linkages if sustainable livelihoods are to be promoted. To this end, participant-led research was conducted in the small-scale traditional commercial linefishery of the southern Cape (South Africa) between Witsand and Mossel Bay. Knowledge and perceptions regarding stressors responsible for changes in the social-ecological system, which ultimately affect the fishers’ ability to fish successfully, were recorded using semi-formal interviews and focus groups with 50 participants. The results presented not only offer valuable insights into the day-to-day experiences of these fishers, but also expose knowledge gaps that exist in micro-scale interactions influencing the fishery system. An analysis of various stressors is presented, which includes the impacts of and responses to climate variability; challenges presented by fisheries policies and regulatory frameworks; social and economic considerations; inadequate infrastructure; and general political considerations. The development of a more comprehensive understanding of stressors that affect the social-ecological system at various scales provides valuable insights into a fishery system that is currently not well described, and provides the basis for analyses into vulnerability and resilience. Significance: Understanding the impact and interplay of stressors at multiple scales is important if sustainable livelihoods are to be promoted. This research provides insight into the day-to-day experiences of fishers whilst exposing knowledge gaps that exist at a micro-scale.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".