The political logics of EU-FLEGT in Thailand’s multistakeholder negotiations: Hegemony and resistance
Bibliographic record
Abstract
The reduction of illegal logging and related trade has been on the international policy agenda since the 1990s. The EU's Forest Law Enforcement, Governance and Trade initiative (EU-FLEGT) seeks to address illegal logging through a scheme that rests on multistakeholder negotiations. However, past initiatives seeking to reform forest governance in the global South have reproduced the uneven outcomes of colonial forest governance by further empowering national government authorities. In the case of Thailand, FLEGT negotiations between November 2013 and April 2021 succeeded in opening a political space for civil society to engage with government actors. However, FLEGT negotiations during this period failed to address the uneven outcomes of forest governance, benefiting elites at the expense of the rural poor due to an 'anti-politics effect. The FLEGT multistakeholder negotiations did not consider the uneven historical relations to land and resource rights nor the intrinsic power dynamics of different actor groups. As such, dominant actors from the government and private sector succeeded in structuring the terrain of the FLEGT negotiations to determine which civil society demands for reforms to tenure and resource rights they would concede, and which they would not.
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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 | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.027 |
| Scholarly communication | 0.018 | 0.008 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".