Finding Common Ground: A Critique of Subsumption Theory and Its Application to Small-Scale Forest Carbon Offsetting in Uganda
Bibliographic record
Abstract
Carbon markets as a policy tool to mitigate emissions of greenhouse gases continue to be controversial, especially in developing countries. It is thus refreshing that Carton and Andersson (2017 Carton, W., and E. Andersson. 2017. Where forest carbon meets its maker: Forestry-based offsetting as the subsumption of nature. Society & Natural Resources 30 (7):829–43.[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]) undertook a field investigation of a transnational forest carbon offset project in Uganda. However, I am concerned that assumptions of structural Marxism that underwrite subsumption theory may have led the authors to see the project as inherently conflict ridden and exploitative and to neglect actual benefits. Their presentation of the project’s local impact jarred with my own empirical research into this project, undertaken in 2009, as well as more recent news accounts. While my field effort preceded the authors’ by 6 years, I attribute our different interpretations largely to theoretical and methodological differences. Evidence I present below suggests considerable alignment between the interests of transnational carbon markets and Ugandan smallholder farmers. Additional fieldwork might be able to resolve these differences in interpretation.
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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 | 0.027 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.010 | 0.099 |
| Scholarly communication | 0.012 | 0.030 |
| Open science | 0.007 | 0.015 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".