Ontological Politics and Conservation in Thailand: Communities Making Rivers and Fish Matter
Bibliographic record
Abstract
Abstract The lens of ontological politics explains the persistence of conflicts between upland ethnic minorities such as Karen peoples and state forest conservation agencies in Thailand. As can be seen with Karen communities in areas managed as national parks, such as the Ngao River basin, the environmental management practices employed by state agencies and ethnic minority communities enact different ontologies of conservation. We argue that shifting the focus of conservation discourse from forests to inland fish could present opportunities for both recognition of and government support for community-based conservation. We demonstrate how state forest conservation agencies foreclose other ontologies, thus precluding community-based conservation. Such ontological dominance, however, is more contested in the case of state agencies with jurisdiction over inland waters. By examining river management and conservation in the Ngao River basin, we consider how these communities make visible the agency of fish and other aquatic life through their knowledges and practices. We argue that Ngao Karen communities have demonstrated that they can account for and conserve aquatic life in inland waters in ways that the Thai state has been unable to do, thus legitimising otherwise marginalised ontologies for ‘resource’ management and conservation throughout Thailand. Abstract in Thai: rb.gy/j0ify
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.036 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.000 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".