Mapping the Ontology and Epistemology of Research Into Forest Carbon Offsetting in Developing Countries
Bibliographic record
Abstract
ABSTRACT In this paper, we consider knowledge cumulation in one of the most polarized areas of environmental governance research: forest carbon offsetting in developing countries. Our specific contribution is a critical review of the ontological and epistemological positioning of 31 studies published in the peer‐reviewed literature on forest carbon offsetting in Uganda. At the surface, differences appear related to methodological gaps along the qualitative‐quantitative divide. However, probing deeper suggests a lack of agreement on fundamental ontological and epistemological issues, which challenges traditional understandings of scientific knowledge cumulation. Among our key findings is that research into forest carbon offsetting in Uganda is predominated by epistemologies we characterize as neopositivist (approximately half) and neo‐Marxist overdetermination (approximately one‐third). Structural ontologies were significantly more frequently identified in our critical review than agentic ontologies, while structure–agency balancing ontologies were the least represented. Notably, research most critical of forest carbon offsetting was characterized by an epistemology of neo‐Marxist overdetermination and structural/synchronic ontology. While recognizing the limits of our critical review into forest carbon offsetting in Uganda, knowledge cumulation appears to be frustrated by a lack of agreement on fundamental ontological and epistemological presuppositions. Nonetheless, given the polarized debate on forest carbon offsetting, delineating such fundamental differences may help lay the groundwork for promoting dialogue between different research traditions. But such epistemic fragmentation or diversity may not in itself constitute epistemic justice, which requires additional attention to broader power imbalances involved in the conduct of environmental governance research in developing countries.
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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 | 0.075 | 0.111 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.028 | 0.019 |
| Science and technology studies | 0.006 | 0.030 |
| Scholarly communication | 0.020 | 0.019 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| 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".