Getting Ahead or Just Enough To Get By? The Limits of Social Capital in an Asset Based Community Development Model
Bibliographic record
Abstract
Recent trends in community development efforts rely on social capital to solve issues at the local level through consensus building, increasing capacity and citizen empowerment. The asset based community development (ABCD) approach assumes relationships and partnerships built on networks of trust and shared norms build communities beneficial for all members. The current community capacity building approach blurs political interests and supports the current neoliberal agenda of the state and private interests to shift the responsibility and management of social problems to the community. This project calls in to question the potential of an assets based community development strategy as it has been attempted in Hamilton, Ontario to lead to long-term structural change in addressing social issues at the root. Findings suggest that despite the number of community projects appearing on the ground, there is little evidence to support asset based community development and social capital that leads to long-term structural change in communities, or economic prosperity to the extent proponents suggest. Furthermore, contrary to the claim of resident leadership, the findings suggest models that attempt to include resident participation are still managed, funded, and administered by professionals in organizations in a ‘top down’ manner. Additional discussion will explore how social capital and capacity can be used toward meeting social justice outcomes in communities.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".