Discourses of Social Inclusion in Sport and Recreation in Rural Ontario
Bibliographic record
Abstract
The benefits and challenges of participating in sport and recreation as a new Canadian are well documented in the existing literature, however, they are typically considered in an urban context. More specifically, a gap exists regarding how sport and recreation practitioners and managers understand social inclusion work in sport and recreation and the impact these understandings may have on newcomer populations who are living in rural and other non-metropolitan communities. The purpose of this study was to gain a more in-depth understanding of how social inclusion is understood in both sport and recreation practice and policy. Further, I sought to critically examine discourses of Whiteness in programming and policy in rural settings. Therefore, in this research, I explored two questions: 1) How do sport and recreation practitioners and managers understand social inclusion in and through sport and recreation in their rural communities? and 2) How do discourses of community and inclusion impact the way sport and recreation practitioners and managers define and understand social inclusion? An instrumental case study methodology was used to explore these questions in a region of Northern Ontario (including Nipissing and Sudbury Districts) and both semi-structured interviews and document analysis were conducted to collect data. A critical discourse analysis (CDA) was used for this research which helped to highlight how discourse functions to construct and transmit knowledge, and the ways this organizes and maintains social institutions (Fairclough 2001; Mogashoa, 2014). I drew from Critical Whiteness theory (CWT) to better understand how discourses of Whiteness are produced and maintained in sport and recreation. The analysis identified three discourses related to social inclusion in sport and recreation: We’re all in this together; ‘They’ aren’t from here; and Whose responsibility is it?. This research highlights how discourses of colourblindness, “othering” of diverse populations, and ambiguity of responsibility for social inclusion work may inform practice and underpin systems of Whiteness in sport and recreation. Additionally, it is important to consider how policies, practices, and understandings of social inclusion work in sport and recreation settings are translated throughout and between organizations. \n
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.028 | 0.022 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".