Empires of Forestry: Professional Forestry and State Power in Southeast Asia, Part 1
Bibliographic record
Abstract
Abstract This paper examines the origins, spread, and practices of professional forestry in Southeast Asia, focusing on key sites in colonial and post-colonial Indonesia, Malaysia and Thailand. Part 1 challenges popular and scholarly accounts of colonial forestry as a set of simplifying practices exported from Europe and applied in the European colonies. We show that professional forestry empires were constituted under colonialism through local politics that were specific to particular colonies and technically uncolonised regions. Local economic and ecological conditions constrained the forms and practices of colonial forestry. Professional forestry became strongly established in some colonies but not others. Part 2, in a forthcoming issue of this journal, will look at the influence on forestry of knowledge and management practices exchanged through professional-scientific networks. We find that while colonial forestry established some management patterns that were extended after the end of colonialism, it was post-colonial organisations such as the FAO that facilitated the construction of forestry as a kind of empire after World War Two. As a sector, forestry became the biggest landholder in the region only after colonialism had ended.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".