The Study of Environmental Affordances to Improve Active Recreation in Edmonton's River Valley
Bibliographic record
Abstract
Urban planners and professionals often turn to the theory of “Environmental Affordances” to understand how specific characteristics of urban spaces either encourage or discourage different behaviours. This theory emphasizes the dynamic interaction between individuals and environments, with people’s needs evolving over time, and the environment needs to be adapted to accommodate these shifts. The case study in this research is the North Saskatchewan River Valley in Edmonton, specifically between the Rossdale and North Shore areas, which is the most populated area used by diverse groups of users for recreational purposes. The goal of this research is to study the environmental affordances indicators that improve active recreation within this area, ultimately proposing strategies to improve the space and optimize the use of the green space for future.This research is based on a mixed method approach, combining both quantitative and qualitative techniques to gather insights from literature reviews, surveys and interviews from different studies, users and professionals.The results illustrate the significance of environmental affordances indicators such as accessibility, safety, natural environment, and diverse amenities that affect improving active recreation. These results also highlight the role of walking and cycling trails, as well as inclusive design elements, in inspiring engagement. It will also emphasize the importance of the physical and social benefits of accessible green spaces that meet community recreational needs. This work offers valuable insights for future urban planning, focusing on the design of inclusive, sustainable spaces. Future research should explore the long-term impacts of these areas on community health and urban sustainability.
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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 | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".