Bibliographic record
Abstract
Since the coup, multinational enterprises have come under pressure to review their operations in Myanmar and exit any relationship they have with militarycontrolled entities.Targeted sanctions imposed by the United States, United Kingdom, Canada and European countries made it a legal requirement in their jurisdictions.However, very few foreign firms were in joint venture or had other commercial relations with military-owned or controlled entities, and the overestimation of the extent and significance of such relationships has distracted policymakers and activists from considering policies focused on the role the business community could play to strengthen human rights in Myanmar.The companies that left Myanmar mostly did so for security, commercial or reputational reasons.Leaving was not always easy or helpful to Myanmar's citizens and, in some instances, even benefited the military.This chapter explores these pressures and responses and argues that policies need to recognise that change will only come from within Myanmar; thus, the focus should be less on external actors and more on what the international community can do to support responsible business practices in the country that will strengthen human rights and the wellbeing of the people.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".