The Human Development Index In Canada: Ranking the Provinces and Territories Internationally, 2000-2015: An Update
Bibliographic record
Abstract
We develop internationally comparable estimates of the Human Development Index (HDI) for the Canadian provinces and territories over the 2000-2014 period. The HDI is a composite index composed of three dimensions (life expectancy, education and income) measured by four indicators (life expectancy at birth, average years of education, expected years of schooling and GNI per capita). We first replicate the Canadian estimates from the most recent Human Development Report (HDR) using data from Statistics Canada. Next, we generate estimates for the provinces and territories following the same methodology and using the same Canadian data sources. We make these estimates internationally comparable by scaling each province or territory’s estimate to Canada’s in the most recent HDR. This allows the provinces and territories to be ranked in the most recent HDR international rankings for all four component variables as well as the overall HDI. The highest HDI score in 2014 among the provinces and territories belongs to Alberta, which would be fourth in the international rankings, while the lowest ranking region is Nunavut, which would be in 46th place. Overall, our report highlights the diverse human development experiences of Canadians that are concealed by Canada’s overall HDI.
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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.030 | 0.070 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".