Cambodia: the thin line between development and human rights during COVID-19
Bibliographic record
Abstract
In the chapter on Cambodia, Natalia Szablewska (the Open University, United Kingdom) Muy Seo Ngouv (Royal University of Law and Economics, Cambodia) and Ratana Ly (University of Victoria, Canada) argue Cambodia has been steadily climbing the Human Development Index since the 1990s and the key dimensions of human development, like income, education and health, have been improving, placing Cambodia in the “medium human development” category among 189 countries and regions (UNDP, 2020). The outbreak of the COVID-19 pandemic, however, impacted the social and economic progress to date. In response to the pandemic, the government introduced a number of measures, including emergency laws, on public health grounds that have had a negative impact on basic human rights by restricting people’s access to the essentials, healthcare or education. The authors examine the legal and policy developments in Cambodia in response to the COVID-19 pandemic over the last two years, adopting a human rights lens to analyse the implementation of COVID-19 measures with a particular focus on the education and business sectors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.015 | 0.026 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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; both teacher heads agree on what is shown here.
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".