Mainstreaming Sustainable Development Goals (SDGs) into Local Development Planning: Lessons from Adentan Municipal Assembly in Ghana
Bibliographic record
Abstract
The Sustainable Development Goals (SDGs), which were adopted in September 2015 represent a challenging worldwide action plan that aims to end poverty, achieve gender equality, in diverse dimensions, promote decent work among others. Global realization of the SDGs by 2030 is highly dependent on the localization and effective implementation of the goals, yet little is known about diverse perspective of SDG localization and challenges involved. It is in response to this that the study examines the magnitude to which SDGs have been integrated into local development planning using Adentan municipal as a case study. A qualitative method with an in-depth interview of 20 key informants was adopted. The study developed a conceptual framework which was used to examine Adentan municipal Assembly on SDG mainstreaming. The study also did a critical analysis of the medium-term development plan of the municipal assembly to identify how the Assembly has effectively mainstreamed the SDGs at the local level. The findings from the study revealed that the authorities are aware of the SDGs. Majority of the targets in SDGs (1,2,3,4,5,6,8,9,10,11,13,14,16 and 17) have been integrated into the local development plan of the Assembly. However, SDG 7 and 15 were of no interest to the municipal. The findings further indicated that financing, low awareness of the relevance of the SDGs among the citizens in the municipality and bureaucracy are the major challenges of SDG mainstreaming at the local level. The study proposed a framework which extends the theory of change on effective SDG mainstreaming and can be added to other existing framework on SDG mainstreaming at the local level to address the challenges and needs of SDG mainstreaming for development initiative and may inform future research in mainstreaming and planning.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".