Supporting social sustainability in resource‐based communities through leisure and recreation
Bibliographic record
Abstract
Abstract Community development can be conceptualized as a balance that exists amongst environmental, economic, and social systems; this achievement, however, is difficult for communities undergoing rapid urban development. In particular, resource‐based communities (RBCs) that develop as a result of investment into the extraction of a natural resource such as oil and gas have been found to experience uneven economic development, environmental degradation, and social instability. A contributing factor to social instability in RBCs is the reliance on a large transient workforce drawn to the area in search of employment opportunities and the inability to provide this burgeoning population with sufficient urban infrastructure and services. Building on previous research that theorized the key to increasing the sustainability of RBCs is to retain a permanent population, this research explores the contribution that opportunities for leisure and recreation makes to resident retention. Using Fort McMurray, Alberta as a case study, results indicate that among a group of people who lack established social ties, the main source of social interaction is participation in leisure and recreation activities. However, traditional activities associated with leisure and recreation did not take into account the unique challenges found in RBCs indicating an adaptable approach to provision is necessary.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.000 | 0.000 |
| 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".