"Giving Back to the Community": Young and Established Leaders' Perspectives of Community Engagement in Sports and Recreation Initiatives Serving Children and Youth
Bibliographic record
Abstract
Increasingly, researchers, practitioners, participants, governments, funders, and others involved with sport are suggesting that sport is good for children and youth – but only under the right conditions . “Good sport” requires careful consideration and targeted action to ensure participants have positive experiences. Caring adults, particularly those who had positive sport experiences in their childhood, can set the stage for “good sport” to happen for children and youth. One way they can do this is to return to volunteer and/or work with the very organizations that provided them with opportunities in their childhood or youth – what we call “giving back”. In this research project, we prepared reports, presentations, and academic manuscripts that detail the “giving back” experiences of young (aged 17-27) and established (aged 28+) adult leaders who engage in sport leadership roles with three community partners offering programs targeting children and/or youth, namely the Boys and Girls Club of Ottawa (BGC Ottawa), la Société des Jeux de l’Acadie (SJA) and the Arctic Winter Games International Committee (AWGIC). In each of the three cases, we discussed young and established leaders’ understandings of “community”. Our analyses also focused on the leaders’ motivations for “giving back” and the meanings they gave to their responsibilities, in addition to and beyond sporting fervor, as practices of engagement and commitment to young people and to the “community” (however they defined it). While completing the analyses, three themes emerged that needed further exploration. These themes include – youth development, sense of belonging and community development, and deep motivations for working in these contexts. These themes are the focus of this report. Highlighting these themes will give our community partners, governments, and funders a sense, within these sports programs, of how volunteers and employees understand their work (with respect to how it helps children and youth develop), how they feel while doing this work, (with respect to sense of belonging and community development) and why they do this work (deep motivations). We also feel a responsibility to share the leaders’ voices about the value of these programs and activities and, ultimately, some of the conversational paths that might help our community partners and other community sport organizations continue to deliver transformative sport programming and events for children and youth.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".