A Scientific Review on the Issues of Sustainability in Vietnam During the Period from 2011 to 2021
Bibliographic record
Abstract
Vietnam is an active member of the United Nations and must make great efforts to implement sustainable development.Through better perception, natural resources, policy, and scientific studies may support the country in developing a hybrid approach to achieve the Sustainable Development Goals (SDGs) convincingly.The review demonstrates that Vietnam has achieved the expected results on most goals.Nevertheless, gaps in regions, socio-economic groups, and gender equality lead to uneven development in some regions.Regional boundaries that function as urban and rural, plains and mountainous, or ethnic minorities also limit approaches to food production, freshwater, and overall policy implementation.There are rising environmental issues that negatively affect long-term development such as pollution (air, water, and soil), salinization due to rising sea levels, climate change, and depletion of natural resources, etc….Owing to rapid economic development, and the lack of awareness of the SDGs among residents and young people, Vietnam has not yet made strong progress toward sustainable development.Appropriately, scientists have paid increasing attention to the quest to interpret insurmountable obstacles to sustainable development to provide policymakers with better and more powerful solutions and response capabilities and help them achieve their overall goals conclusively.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".