2019 Nova Scotia Quality of Life Survey
Bibliographic record
Abstract
Measuring What Matters In May and June of 2019, 80 000 Nova Scotia households received an invitation in their mailboxes to participate in a first-of-its-kind survey measuring quality of life across the province. The survey asked how people feel they are doing in eight key areas related to their quality of life. Our research partners, the Canadian Index of Wellbeing, have compiled the information collected from the survey into data sets and reports. The summary results were released in March 2020 and additional reports are scheduled for release later in the year. These reports will serve as the foundation for innovative approaches to priority setting and planning at a local level for years to come. The map below shows where those regional groups are. Results The survey focused on alternative measures of progress, beyond GDP, and explored how Nova Scotians are doing in eight areas that affect their wellbeing: community vitality, living standards, healthy populations, democratic engagement, leisure and culture, time use, the environment and education. Learn more about the Eight Domains of Wellbeing. 12,827 Nova Scotians participated in our 230-question survey. This confirms our belief that they are behind the project.
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.010 |
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".