Blending Human-Centred Design and Community-Based Participatory Action Research Approaches: Designing Community Sport and Recreation Provisions for Equity-Owed Communities
Bibliographic record
Abstract
Addressing the social and economic disparities equity-owed low-income residents experience that prevent participation in community sport and recreation is complex. Community-based participatory action research (CBPAR) has been utilized to facilitate the participation of equity-owed communities in sport and recreation initiatives. However, in this paper, we discuss how CBPAR and human-centred design (HCD) help engage communities in discourse and action to support innovative social change in the context of sport and recreation for equity-owed low-income communities. This paper compares processes, core principles, and outcomes of CBPAR and HCD. It highlights how they can collectively drive discourse and action to foster innovative social change in sport and recreation for equity-owed communities. The proposed integration, called CBPAR+HCD, is suggested to initiate solutions that address the multifaceted challenges through a social justice lens, placing community-driven social innovation at the forefront. This paper highlights the benefits of combining CBPAR+HCD and acknowledges the inherent challenges in implementing this dual approach. Furthermore, it offers recommendations to support the combined approach, emphasizing the importance of this integrated methodology in promoting social change and addressing inequities within community sport and recreation initiatives.
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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 | 0.169 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.009 | 0.031 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".