“Community Cup, We Are a Big Family”: Examining Social Inclusion and Acculturation of Newcomers to Canada through a Participatory Sport Event
Bibliographic record
Abstract
While sport is widely understood to produce positive social outcomes for communities, such as the inclusion of diverse and marginalized groups, little researched has focused on the specific processes through which these outcomes may or may not be occurring. In this paper, we discuss the Community Cup program, and specifically a participatory sport event which seeks to connect newcomers to Canada (recent immigrants and refugees) in order to build capacity, connect communities, and facilitate further avenues to participation in community life. For this research, we worked collaboratively with the program to conduct an intrinsic case study, utilizing participant observation, document analysis, focus group, and semi-structured interviews. We discuss how the structure and organization of the event influences participants’ experiences and consequently how this impacts the adaptation and acculturation processes. Using Donnelly and Coakley's (2002) cornerstones of social inclusion and Berry’s (1992) framework for understanding acculturation, we critically discuss the ways that the participatory sport event may provide an avenue for inclusion of newcomers, as well as the aspects of inclusion that the event does not address. While exploratory in nature, this paper begins to unpack the complex process of how inclusion may or may not be facilitated through sport, as well discussing the role of the management of these sporting practices. Furthermore, based on our discussion, we offer suggestions for sport event managers to improve the design and implementation of programming offered for diverse/newcomer populations.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.029 | 0.011 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".