Local ecological knowledge and the impacts of global climatic change on the community of seaweed extractors in Pisco-Perá
Bibliographic record
Abstract
Global climate change implies difficulties for coastal communities where activities are highly influenced by climate. This paper examines the case of seaweed harvesting in the community of Pisco-Peru. Aspects of environmental change that impact seaweed harvesting include global warming, ldquoEl Nintildeordquo events, pollution of marine space, declines of marine species, and the rupture of ecological cycles. We look for relationships between local ecological knowledge (LEK) related to climate and other environmental change and strategies for coping with and adapting to current and anticipated change. This project is developed through a participative methodology, with the participation of university researchers and the community of seaweed extractors, and builds on an ongoing study of collaborative approaches to research and development of the algae industry in this region. Research questions include: the nature of the LEK held and shared; the extent to which LEK includes: the effects of climate changes on resources, harvesting and communities; and the contribution of LEK to industry resilience, harvester livelihoods and community well-being. The results of the research provide insight into LEK accumulation about algae species, management, and impacts of global environmental change. Documenting methods of collecting, analyzing and sharing harvester knowledge is an additional contribution.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".