Access to Recreation in Rural Communities: Municipal Recreation’s Approaches to Supporting Citizens Living with Low Incomes
Bibliographic record
Abstract
Rural communities often have fewer resources and supports to cultivate recreation participation, making participation in recreation less accessible for citizens with low incomes. Such barriers highlight the important role rural recreation practi-tioners have in terms of developing and delivering access provisions for these citi-zens. The objectives of this study are as follows: 1) explore the access provisions recreation practitioners design and deliver to rural citizens with low incomes, and 2) discuss the implications of the access provisions in relation to the rural con-text and citizens with low incomes’ participation in recreation. This paper focuses on research conducted in 2019-2020 in Atlantic Canada. The data were collected through semi-structured interviews with 16 municipal recreation practitioners. Through the social ecological framework, the results revealed three access provi-sions designed for and delivered to rural citizens with low incomes: 1) one-on-one support for programmatic processes; 2) flexibility with financial policies and programs; and 3) building reach, relevancy, and capacity: the role of community partnerships. Based on these findings, this study addresses the benefits of these provisions in terms of supporting access to recreation for citizens with low in-comes residing in rural communities. Considering the rural landscape, we recom-mend access provisions in rural communities encompass more personal, flexible, and community approaches to support access to recreation for citizens of limited financial means.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| 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".