Forest Grabbing Through Forest Concession Practices: The Case of Guyana
Bibliographic record
Abstract
Colonial governments asserted sovereignty and property rights gradually over the territory of Guyana, disregarding pre-existing Indigenous Rights. Although a Forest Department modelled on the Indian Forest Service was established, there was no equivalent settlement process to determine the rights of forest peoples. State Forest area is declared by administrative fiat. These two elements have enabled State-endorsed forestland grabbing. Logging was scattered and selective until the early 1980s. A neoliberal economic program from the 1980s has allowed Asian companies to gain control over at least 80 per cent of large-scale forestry concessions, equivalent to one-third of the 15.8 million hectares of State-administered public forests. The relative success of the Asian companies can be understood in terms of available capital, willingness to invest, knowledge of markets, and willingness to corrupt. The relative failure of the pre-existing Guyanese-owned businesses can be understood in terms of lack of capital, inability to save and unwillingness to invest, lack of knowledge of marketing, and lack of cooperation within the sector. Some conclusions from the Guyana story are relevant to other countries related to resource-hungry transnational enterprises.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".