Bibliographic record
Abstract
In this paper, we examine the extensive use of bans (temporary prohibitions or moratoriums) on resource exploitation activities by the government of Laos as an authoritarian environmental governance tool. We focus on bans enacted recently in three sectors: on the granting of land concessions in 2012, on the expansion of banana plantations in 2014, and on logging exports in 2016. Bans have long been used in Laos, particularly in the forestry sector, despite their considerable political risk and economic costs, the way they contradict state actors’ promotion of these same activities as drivers of development, and their past ineffectiveness. Most cases in the environmental authoritarian literature explore authoritarian states with a strong capacity to employ top-down governance tools. We argue, in contrast, that the Lao government’s repeated use of bans instead of other effective governing tools, such as more incremental, conditional, or incentive-based policies, reflects not strong state capacity but rather the limits to its implementing and enforcement capacity. The bans examined emerge from central–local divides, unregulated village land leasing, and failures to extract state revenues, and we interpret them as central-state efforts to consolidate and assert a more centralized, command-and-control authority over the country’s land and resources.
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 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".