Localisation of Sustainable Development Goals (SDGs) in Bangladesh: An Inclusive Framework under Local Governments
Bibliographic record
Abstract
To accelerate the implementation of SDGs at the national level, policymakers and practitioners are focusing on localisation, where the local government (LG) can play a critical role. This paper examines the LG’s capacity and its existing link with the implementation of SDGs at the local level in Bangladesh, and it offers an inclusive framework for the SDGs’ localisation. The data was gathered through an in-depth interview of 10 chairmen of the Union Council (lowest tier of LG) in Northern Bangladesh’s Nilphamari district. An SWOT analysis of the local government was conducted to determine the organisation’s effectiveness and capacity in light of its vulnerabilities, threats, strengths, and opportunities. The data indicate that while the majority of LG representatives have some knowledge, participation, and perceptions about the SDGs, they demonstrate a great desire to gain additional knowledge and participation. The study ascertains SDG 1 (No poverty), SDG 2 (Zero hunger), and SDG 6 (Clean water and sanitation) as the most locally important SDGs relevant to the LG’s actions, based on the opinions of the surveyed respondents. The LG’s strengths were identified in their familiarity with local problems and the environment, as well as the presence of potential local stakeholders, while their weaknesses included a lack of capacity, resources, funding, and a lack of decentralisation and empowerment of the LG. This study develops an inclusive framework for the localisation of the SDGs under the leadership of LGs based on the findings. To expedite the localisation of the SDGs in Bangladesh, the framework recommends forming an SIC (SDG implementation committee) by including all key local stakeholders, and asking the national government to increase local competence and resources through an appropriate decentralisation of the LG.
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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 | 0.025 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.010 | 0.030 |
| Scholarly communication | 0.019 | 0.016 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.004 | 0.004 |
| 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".