Government-NGO relationship: regulatory constraints and uncertain policy implementation in Vietnam
Bibliographic record
Abstract
This research examines regulatory constraints and uncertain policy implementation on NGO (non-governmental organization) operations in Vietnam, and draws lessons for NGOs, the government, and donors. Research questions were answered through qualitative methods. Twenty semi-structured interviews were conducted, thirteen with the representatives of NGOs, and seven with high-ranking government officers. In addition, documents (decisions, proposals, reports) were reviewed. The government of Vietnam continually impose regulations intended to govern NGO operations through Decree 12/2012/ND-CP, Decision 76/2010/QD-TTg., and Decree 93/2009/ND-CP. Policy implementation is uncertain and unpredictable, making government-NGO relations unhealthy. NGOs should educate the government and donors, increase the effectiveness of implementation, and acquire knowledge about government operations. The government needs to strengthen awareness about NGOs’ operations, and improve the regulatory process. Donor-NGO communications should be strengthened. In summary, the political imposition results in unhealthy government-NGO relations. Government-NGO-donor communication should be improved so that NGOs can benefit society.
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 | 0.015 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".