Bibliographic record
Abstract
Foreword: John P. Kretzmann Introduction Section I: Communities mobilizing assets and driving their own development 1 Possibilities for income-deprived but capability-rich communities in Egypt 2 God created the world and we created Conjunto Palmeira: four decades of forging community and building a local economy in Brazil 3 Building the Mercado Central: Asset Based Community Development and community entrepreneurship in the USA 4 The Jambi Kiwa story: mobilizing assets for community development in Ecuador 5 When bamboo is old, the sprouts appear: rekindling local economies through traditional skills in Hanoi, Vietnam 6 By their own hands: two hundred years of building community in St Andrews, Nova Scotia, Canada 7 The hardware and software of community development: migrant infrastructure projects in rural Morocco 8 A spreading banyan tree: the Self Employed Women's Association, India 9 People's institutions as a vehicle for community development: a case study from Southern India 10 Jansenville Development Forum: linking community and government in the rural landscape of the Eastern Cape Province, South Africa Section II: ABCD in Ethiopia, Kenya and the Philippines 11 Stimulating Asset Based and Community Driven Development: lessons from five communities in Ethiopia 12 Reviving self-help: an NGO promotes Asset Based Community Development in two communities in Kenya 13 From DCBA to ABCD: the potential for strengthening citizen engagement with local government in Mindanao, the Philippines 14 Conclusion Index
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.201 | 0.107 |
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".