Bibliographic record
Abstract
Abstract In this essay, we highlight the intellectual context that shaped our initial conceptualisation of political forests as dynamic spaces and political ecologies, and how our fieldwork and comparative approach shaped our subsequent elaboration of the concept and its empirical manifestations. Of particular significance was our emphasis on incorporated/relational comparison and our multiscale analysis. These approaches allowed us to locate subjects and processes in specific field sites within an emergent global forestry network produced through multiscale interactions and movements within and among colonial and FAO forestry empires. We revisit the key processes through which we learned to see common and contrasting mechanisms that have made forests inherently political in our six research sites in Indonesia, Malaysia, and Thailand, linking these “classic” mechanisms to concepts in wide use today. These concepts include understanding political forests as co‐produced, the significance of expertise in their reproduction, and the interactions between politics and the lively materialities of political forests. Among other conclusions, we suggest that the political forest is being replaced by what could be called “political conservation”, which has its own knowledge networks and expertise that displace but also build on political forestry. Finally, we reflect on how these ideas are being further developed by the authors in this symposium, whom we gratefully acknowledge for demonstrating that the politicisation of forests continues to be significant today.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".