Introduction: New Frontiers of Ecological Knowledge: Co-producing Knowledge and Governance in Asia
Bibliographic record
Abstract
This essay makes a case for centering the questions of ecological knowledge in order to understand how environmental governance and resource access are being remade in the frontier ecologies of Asia. These frontiers, consisting of the so-called uplands and coastal zones, are increasingly subject to new waves of extractive and conservation activities, prompted in part by rising values attached to these ecologies by new actors and actor coalitions. Drawing on recent writings in science and technology studies, we examine the coproduction (Jasanoff 2004) of ecological knowledge and governance at this conjuncture of neoliberal interventions, land grabs, and climate change. We outline the complex ways through which the involvement of new actors, new technologies, and practices of boundary work, territorialisation, scale-making, and expertise transform the dynamics of the coproduction of knowledge and governance. Drawing on long term field research in Asia, the articles in this special section show that resident peoples are often marginalised from the production and circulation of ecological knowledge, and thus from environmental governance. While attentive to the entry of new actors and to the shifts in relations of authority, control, and decision-making, the papers also present examples of how this marginalisation can be challenged, by highlighting the limits of boundary-work and expertise in such frontier ecologies.
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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 | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".