Identifying Financially Remote First Nations Reserves
Bibliographic record
Abstract
Chen et al. (2021) show that almost one-third of First Nations band offices in Canada are within 1 kilometre (km) of an automated banking machine (ABM) or financial institution (FI) branch and more than half are within 5 km. Further, over three-quarters of band offices are within 20 km of an ABM or FI branch and almost 90% are within 50 km. We focus on 49 First Nations locations that are more than 100 km away from an ABM or FI branch or do not have an identifiable travel route (by road or boat) to an ABM or FI branch. We refer to these First Nations as financially remote. We show that these locations have small populations and limited access to internet and mobile services. As a result, these First Nations have poor access to cash sources and physical delivery of financial services as well as limited access to digital payments and electronic banking. We also assess the remoteness of these locations according to an alternative method based on measures of agglomeration (community population) and proximity to other communities. We find that, according to this measure, these 49 financially remote First Nations are generally among the most geographically remote communities in Canada. Further, we show that these First Nations are also among the lowest scoring communities in Canada according to a measure of community well-being based on indicators of educational attainment, labour force activity, income and housing. The geographical remoteness of these 49 First Nations, their small populations, limited infrastructure and digital services, and relatively low community well-being all likely contribute to their poor access to cash and financial services.
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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 | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".