Citizens as community experts: The benefits of a neighborhood leadership program
Bibliographic record
Abstract
Referring to citizen leadership and expertise in participatory processesat community level, research emphasizes the necessary balance betweeninclusiveness and knowledgeability. Both conditions provide efficiency andlegitimacy for policy making, so as to avoid governance based either on elites,or on mass democracy. Scholars propose the inclusion of “expert citizens”as mediators between scientific and lay knowledge. In a practical manner, thisis to be achieved by the formation of community leaders, able to address theneeds and improve the quality of life for residents. A neighborhood leadershipprogram develops capacities in human relations, public speaking, conflictresolution, rebuilding of trust and acting inside a network, as well as practicalskills related to grant writing, fundraising or completing a community project.To what extent is the implementation of a professional training programable to provide sustainable solutions in the field of community development?The study addresses this research question by means of a review, synthesisand analysis of previous literature. Its empirical section approaches threeparticular case studies, from the United States and Canada. Results show thatthe initiative of training citizens to become community leaders has positiveeffects at an individual and collective level - it fosters personal development,mutual understanding and social cohesion, contributing to an ongoingeducational process.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".