An Infrastructure Index for Remote Indigenous Communities
Bibliographic record
Abstract
This report sheds light on the deficiencies in infrastructure faced by Canada’s remote Indigenous communities by quantifying the level of infrastructure in 236 remote communities in Canada’s North. This quantification is done through a composite index based on 13 infrastructure indicators, including availability of broadband, roads, airports, the electrical grid, health care, education, water, and housing, with values ranging from 0 to 1. This report compares the level of infrastructure found in remote Indigenous communities both with remote nonIndigenous northern communities and southern cities. Indigenous communities are broken down by the three heritage groups: First Nations, Inuit and Métis. While the southern cities identified in the 2016 Census as Census Metropolitan Areas have an average index score of 0.97, remote Indigenous communities saw a score of 0.45 and remote non-Indigenous communities a score of 0.82. Inuit communities face the lowest level of infrastructure (an index score of 0.31), and remote Indigenous communities in Nunavut fared the lowest of the jurisdictions with a score of 0.30.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| 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) | 0.004 | 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".