How to promote democracy in the ASEAN region The Cambodian example
Bibliographic record
Abstract
'Cambodia is a useful example in some ways for the promotion of democracy in ASEAN; in other ways it is not. Over the first quarter of this century, Cambodia suffered greatly, perhaps more than virtually any country in the world. War, genocide, political oppression, and international isolation all contributed to one of the bloodiest, most turbulent periods that any country has ever endured. Yet today, as Thomas Hammerburg, the UN Secretary General's Special Representative for Human Rights in Cambodia wrote in 1996: 'Cambodian society has made truly remarkable progress since 1993. Within three years after the formation of the Government, Cambodia has become one of the freest countries in the region.' What is responsible for this remarkable transformation? The short answer is a great deal of effort, by Cambodians and by the international community. But I would like also to emphasize that the democracy that emerged in Cambodia is still very fragile. Many people are working to ensure its survival, but that is not an assured outcome. The author will tell about some of the obstacles, and some of the successes, in these efforts.' (author's abstract)
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 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".