Relative Rankings of Communities in New Brunswick Using Community Well-Being Indicators from the Census
Bibliographic record
Abstract
We examine a set of well-being measures for New Brunswick communities over a 15- year period (2001-2016). Using Canadian Census data at the subdivision level, we construct a community-level well-being index which includes the domains of income, education, housing, and employment. Our results show that communities in the top quartile of the well-being index tend to be in southern New Brunswick around the population centres of Moncton, Fredericton, and Saint John. In contrast, communities in the bottom quartile are in the eastern and northeastern parts of the province (e.g., the Acadian peninsula). Patterns for each domain are quite similar except for housing, where communities normally in the upper portion of the well-being distribution – around the population centres of Moncton, Fredericton, and Saint Andrews – tend to rank lower on this domain. Additionally, with respect to education, communities in the northern part of the province typically fare worse than those in southern New Brunswick, with some exceptions around Perth- Andover and Edmundston. Finally, we demonstrate that the distribution of these well-being indicators has remained remarkably stable over this 15-year period: communities at the top and at the bottom of the distribution have remained in these respective positions from 2001 to 2016.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".