Historical perspectives on contemporary human–environment dynamics in southeast Africa
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it