Measuring community engagement: A case study of Livingston (Calgary, Alberta)
Bibliographic record
Abstract
This case study focuses on developing an objective, baseline understanding of the opinions and behaviours of residents currently residing in the developing neighborhood of Livingston (Calgary, Alberta, Canada). Livingston is unique because it is a new community composed of mostly young couples from diverse backgrounds who have collectively chosen to be part of a community focused developing neighborhood. More specifically, researchers from Mount Royal University (MRU) explored the sense of belonging, physical activity behaviors, and level of engagement of the pioneer residents (first families who opted to move in to this brand-new community). This baseline knowledge can be used by the developer (Brookfield Residential) and local recreation leaders (such as Vivo for Healthier Generations and City of Calgary Recreation) to understand the current and anticipated expectations of residents living in Livingston, including opportunities for active and healthy living. Discoveries from this study can also provide evidence to help inform future planning and decision making related to the built environment and vibrancy within a community as it is being built. A combination of semi-structured interviews with families living in Livingston along with personalized physical activity (PA) behaviour assessments and validated questionnaires for both children and adults living in Livingston were used. Several themes emerged from the data analysis including (but not limited to); neighborhood vibrancy, environmental appeal, health and happiness, harmonious living, belonging, and infrastructure.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.022 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".