Harnessing ‘communities of practice’ for local development and advancing the Sustainable Development Goals
Bibliographic record
Abstract
The United Nations Sustainable Development Goals (SDGs) seek to address some of the most critical contemporary global challenges in a 17-goal agenda, including poverty, inequality, global environmental and climate crises. Central to these goals is Goal 17, ‘Partnership for the SDGs’ which forms the core framework for implementation of SDGs across scale. After the first five years of implementation, remarkable progress has been recorded globally; however, significant gaps remain across world regions. For Goal 17, while lateral partnerships and sectoral collaborations have improved, there is little synergy at the local level especially in sub-Saharan Africa. Using the ‘communities of practice’ concept and reflecting on Ghana’s local development framework, this paper contributes to the ongoing discourse on how best to localize global goals, and discusses the relevance of collaboration at the local level in the implementation of SDGs. Our analyses show weak integration of the SDGs into Ghana’s local development structures and poor collaboration among actors at the local level. Given these findings, it is crucial to reprioritize SDG-17 in this last decade of the SDGs by mainstreaming the Goals into statutory local development structures while promoting collaboration and partnerships in ‘communities of practice’ at the local level – where implementation of development occurs.
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 | 0.028 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".