Defining the Anthropocene tropical forest: Moving beyond ‘disturbance’ and ‘landscape domestication’ with concepts from African worldviews
Bibliographic record
Abstract
How natural and cultural forces shaping tropical forested landscapes are conceptualised is of vital importance to Anthropocene debates. We examine two concepts: disturbance and landscape domestication. From the perspective of disturbance, humans – whether ancient or modern – are a priori negative for tropical forests, outside of and alien to nature. From this view, the Anthropocene is a planetary scale aggregation of disturbance. A more just vision of tropical forests, accepting anthropogenic influence on biodiversity, would combine ‘disturbance’ with other concepts that capture human agency and intentionality. Landscape domestication proposes that humans can shape ecology and plant and animal population demographics, making the landscape more productive and congenial for humans, upgrading or degrading the biodiversity of tropical forests. Herein, forest peoples shape the Anthropocene itself through their ‘domestication’ of the forest. Yet this approach can overdetermine culture, ignoring non-human agency, whilst human impacts can be seen as the outcome of intentional modifications to increase landscape productivity, at worst a disavowed projection of ‘economic man’. Using the convivial scholarship framework of Nyamnjoh, we argue that these ideas give incomplete views of tropical forests in the Anthropocene and can be enriched by concepts derived from African worldviews with ‘relationality’ and ‘wholeness’ at their core. These are expressed in ohanife, deriving from Igbo language, ubuntu, from the Nguni language and ukama, a notion from Shona culture. Together these concepts evince an ‘eco-bio-communitarianism’ embracing humans, God, spirits, ancestors, animals and inanimate beings in a ‘community of beings’ irreducible to the culture-nature divide (moving beyond disturbance) and allowing for the agency and personhood of non-humans (moving beyond historical ecology). This is consonant with Indigenous Amazonian worldviews, such as that of Kopenawa. Approaching human-nature relations from Nyamnjoh’s idea of conviviality, we elaborate a less incomplete and more just perspective on the cultural and natural shaping of Anthropocene tropical forests.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.029 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".