Historicizing the carbon forest: colonial residue, scientific forestry and the making of ‘Nigeria’s last rainforest’
Bibliographic record
Abstract
The novelty claims in carbon forestry often obscure the complex histories and the colonial entanglements of carbon forest socioecologies. This paper argues that the conditions of possibility of carbon forestry in ‘Nigeria’s last rainforest’ are tightly linked to the uneven colonial production of forests across Southern Nigeria. Drawing on archival research, ethnographic fieldwork and analysis of program documents and academic literature, the paper unsettles claims of novelty in Nigeria’s carbon forestry by demonstrating the material continuity between colonial forestry and carbon forestry. Focusing on the development of colonial forestry in Southern Nigeria under British colonial rule, the paper traces the coloniality of scientific forestry as a form of environmental rule, and its entanglements with imperial capitalism and presumptions of racial hierarchy. If the success of scientific forestry in Southern Nigeria meant the draining of Nigeria’s forests as timber export, its failure in Cross River paradoxically produced ‘Nigeria’s last rainforest,’ a literal ‘colonial residue’ . In colonial forestry, as in contemporary carbon forestry, a reductionist knowledge of forests, capitalist interests and a racialized global division of labor all interact in consequential ways. The paper concludes that decolonizing Nigeria’s forestry is a precondition for saving its ‘last rainforest.’
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".