Issues in evaluating the community benefits of social interventions
Bibliographic record
Abstract
Across multiple disciplines concerned with community development there is growing interest in the phenomenon of community capacity building; activities, resources and support strengthening the abilities and skills of individuals and groups to take action and to lead the development of their own communities. This book is a valuable resource that presents the latest research into the social and educational issues involved and offers new insights into effective strategies and outcomes for both specific and global communities. Through experiences in Australia and drawing on examples of good practice internationally (including the UK, US, Canada, Europe and New Zealand), the contributors, themselves a mixture of academic researchers and community members, examine with great breadth and depth how regional and rural communities sustain themselves for equitable and prosperous futures and how community members and university academics can create useful knowledge together. This text uniquely brings community capacity building to life through the personal involvement of academic staff in the community as active partners helping to createnew knowledge. [Book Synopsis]
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 | 0.424 | 0.637 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.007 | 0.023 |
| Scholarly communication | 0.026 | 0.021 |
| Open science | 0.010 | 0.010 |
| Research integrity | 0.013 | 0.009 |
| Insufficient payload (model declined to judge) | 0.017 | 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".