“It’s a ripple effect”: the role of intergroup contact within an inner-city youth sport for development and peace program
Bibliographic record
Abstract
Rationale: Positive contact between divided groups experiencing conflict may not only change intergroup attitudes, but may also promote positive relationship development and other community development outcomes.Purpose: The study draws on SDP literature and contact theory to investigate factors that facilitate positive intergroup contact within an inner-city youth SDP soccer program developed within the public sector. A secondary purpose is to examine the impact that contact has on program stakeholders including youth and authority figures within a community plagued with issues of youth deviance.Method: Semi-structured interviews were conducted with thirteen program stakeholders in one municipality in Ontario, Canada.Findings: Relevant programing, understanding others’ perspectives, having champions of youth development, and human, financial, and partnership resources all play a critical role in facilitating contact within an inner-city youth SDP program. Intergroup contact also had many positive impacts on program stakeholders as trusting relationships were developed, youth behavior changed, and community pride was established.Practical Implications: Managers and policy makers can consider these factors when facilitating contact between diverse groups.Research Contribution: The study offers new insight into the role of public sector actors in SDP practice.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".