Social Change: Toward an Informed and Critical Understanding of Social Justice and the Capabilities Approach in Community Psychology
Bibliographic record
Abstract
Community psychology has long been concerned with social justice. However, deployments of this term are often vague and undertheorized. To address this weakness in the field's knowledge body we explored John Rawls's theory of social justice and Amartya Sen's economic theory of the capabilities approach and evaluated each for its applicability to community psychology theory, research, and action. Our unpacking of the philosophical and political underpinnings of Rawlsian theory of social justice resulted in identifying characteristics that limit the theory's utility in community psychology, particularly in its implications for action. Our analysis of the capability approach proposed by Amartya Sen revealed a framework that operationalizes social justice in both research and action, and we elaborate on this point. Going beyond benefits to community psychology in adopting the capabilities approach, we posit a bi-directional relationship and discuss how community psychology might also contribute to the capabilities approach. We conclude by suggesting that community psychology could benefit from a manifesto or proclamation that provides a historical background of social justice and critiques the focus on the economic, sociological, and philosophical theories that inform present-day conceptualizations (and lack thereof) of social justice for community psychology.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.002 | 0.016 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 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".