Bibliographic record
Abstract
belongingness in differing ways; nurturing all three forms of social capital provides a balance of health, inclusiveness and civic engagement for the individual and the community as a whole.Community-Based Participatory Research and Development approaches to neighbourhood development, such as Asset-Based Community Development (ABCD), offer a means to support social capital and capacity development.Additionally, these approaches create space for meaningful opportunities to effect change, supporting personal and neighbourhood agency and setting the stage for future action.From the jurisdictional scan, three priorities in neighbourhood development from a municipal perspective emerged: social capital development, capacity building, and citizen engagement.While every municipality was unique in the practices they employed common initiatives included block party programs, micro-grant incentives, capacity building toolkits and neighbourhood planning activities.Multiple eclectic models were used in guiding these municipal strategies for neighbourhood development; however, five basic models were identified: the Liaison model, Neighbourhood Action model, Networking model; Neighbourhood Planning model, and Resiliency model.These findings were considered within the context of the City of Kelowna and the Active Living & Culture Division's mandates and priorities and the Strong Neighbourhoods program capacity.The researcher proposes that an ABCD approach to the neighbourhood work be adopted.Using the ABCD approach as a guide and the three focus areas for action, a framework to support future decision making and new initiatives has been proposed.This framework is based on an eclectic melding of the five identified models, feature three key elements for fostering strong neighbourhoods in Kelowna:Ultimately, this project builds upon the positive work already being done by the City of Kelowna.As the Community and Neighbourhood Services team contemplates the next steps for the Strong Neighbourhoods program, the recommendations and proposed framework will serve as a springboard for future decision making and development of initiatives.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".