Improving Human Development in Cities through Community Development Partnerships
Bibliographic record
Abstract
Whether we are community leaders, government administrators, or academic researchers, we need to learn from each other how to improve our community development efforts. We have no choice. The biggest problem facing human-ity is this: how can people live in harmony and co-operate to improve their lives? Successful community development efforts can play an essential role in this task. My aim has been to present the framework I use when I consider how to be more effective and efficient in ad-dressing human development needs in my city and my country. (1) What do we mean by “community development”? (2) A framework for thinking about community development partnerships. (3) Where do households obtain resources to meet their needs? (4) The role of civil society organizations in the 21st century. (5) Communities must organize and plan. Conclusion: Civil society organizations engaged in community development processes can form partnerships with the other sectors in society to further the well-being of the population. Without civil society organizations, the state, the private sector, and individual households cannot anticipate and meet all people’s important needs. The state and the private sector must provide new forms of support for the development of a large and diverse range of civil society organizations that promote social and human development at the local level.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.004 |
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".