Relationship between the demographic characteristics of park users and intensity of park use: the case of Stanley Park and Queen Elizabeth Park
Bibliographic record
Abstract
Parks are among the few urban infrastructure that functionally combines all the three pillars of sustainable development namely: ecological, social and economic functions. For example, Stanley Park currently serves as one of the largest tourist destinations in Canada. This helps to promote economic growth through the money spent by tourists in the City of Vancouver. The park also provides ecological services through its green infrastructure whilst at the same time serving as a place for social activities such as cycling, jogging and playing tennis. Despite the enormous benefits derived from urban parks, there is a paucity of research investigating the individual demographic characteristics that tend to associate with increased utilization of public parks within an urban setting. There is therefore the need for park researchers and administrators to understand the relationship between the demographic characteristics of park visitors and intensity of park use. The data used for this research was collected through a survey conducted at Stanley Park and Queen Elizabeth Park, both located in Vancouver, British Columbia, Canada. Chi-square tests were used to assess the association between individual demographic characteristics and increased utilization of public parks. For Stanley Park, place of origin and age were the most important predictors for high park patronage; while employment status and sex were found to be the significant factors that associated with high intensity use of Queen Elizabeth Park. The study shows that different demographic variables influence the intensity in the utilization of Stanley Park and Queen Elizabeth Park. Park administrators and policy makers must therefore undertake park specific needs assessment when providing park facilities, programs and services. This will help promote effective and efficient park service delivery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".