Using crowdsourced data to assess the relationship between neighbourhood-level deprivation and the availability of inclusive leisure programmes in Canadian cities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Monitoring neighbourhood-level access to resources can inform improved urban health. Big data approaches have shown some promise in capturing access to spatial resources such as green spaces, housing, amongst others. However, it is often difficult to capture resources that are not spatially observable such as programmes. For this project, we linked data from a digital listing of inclusive leisure programmes to data on neighbourhood-level deprivation, to explore the relationship between both factors, and how to strengthen approaches for capturing access to health-promoting programmes. Using cross-sectional secondary data analysis, we linked information on material and social deprivation levels in three major census metropolitan areas of Canada to information on the availability of adaptive leisure programmes as listed on the Jooay App (www.jooay.com). Higher availability of inclusive leisure programmes was directly linked to higher social deprivation and inversely linked to higher material deprivation. Inclusive leisure programmes were more available for populations with physical and intellectual impairments and autism spectrum disorders, than sensory and behavioural challenges. Our study suggests potentially differing relationships between forms of deprivation and the availability of inclusive programs and a need for stronger consideration of disability diversity. We also note considerations for using big data to inform urban health.
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.
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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it