A GIS methodology for mapping regional and community vitality for Canada using the CanEcumene 3.0 Geodatabase with census data
Bibliographic record
Abstract
Many ecosystem-based management (EBM) and related applications require integrating geospatial information about socio-economic conditions of human populated areas within a study area. However, integrating socio-economic data in such a way that it can be related to ecological data is challenging due to issues associated with spatial representation between socio-economic and ecological data frameworks. In Canada, this problem is particularly acute given its large geographic size, diversity of environments and highly irregular population distribution. Although several indices have been developed for Canada related to well-being and vulnerability, their suitability for EBM-related applications is limited. This article presents a GIS-based methodology for mapping regional and community vitality index (RVI/CVI) for Canada using standard Census data integrated with the CanEcumene 3.0 Geospatial Database (GDB). The method uses percentile ranks of five sub-indicators of vitality covering population growth, age structure, education, employment and economic wealth. Results reveal a number of notable patterns and trends in socio-economic conditions across the country and across different types of communities and regions. Most notable are decreasing CVI values from economic core regions to rural and remote communities; decreasing scores from high population centres to lower populated areas and lower scores for Indigenous communities when compared with non-Indigenous communities. A series of maps show variation in RVI/CVI values for specific locations with changes over time.
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) | 0.000 | 0.001 |
| Bibliometrics | 0.009 | 0.018 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".