Historical perspectives on contemporary human–environment dynamics in southeast Africa
Bibliographic record
Abstract
The human communities and ecosystems of island and coastal southeast Africa face significant and linked ecological threats. Socioecological conditions of concern to communities, governments, nongovernmental organizations, and researchers include declining agricultural productivity, deforestation, introductions of non-native flora and fauna, coastal erosion and sedimentation, damage to marine environments, illegal fishing, overfishing, waste pollution, salinization of freshwater supplies, and rising energy demands, among others. Human-environment challenges are connected to longer, often ignored, histories of social and ecological dynamics in the region. We argue that these challenges are more effectively understood and addressed within a longer-term historical ecology framework. We reviewed cases from Madagascar, coastal Kenya, and the Zanzibar Archipelago of fisheries, deforestation, and management of human waste to encourage increased engagement among historical ecologists, conservation scientists, and policy makers. These case studies demonstrate that by widening the types and time depths of data sets we used to investigate and address current socioecological challenges, our interpretations of their causes and strategies for their mitigation varied significantly.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| 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".