Bibliographic record
Abstract
No existing remoteness index can be considered to be truly universal. Remoteness has been defined in many different ways by scholars from a variety of areas of study, and indices that measure remoteness are vital in guiding policy decisions in remote regions. However, index methodologies vary greatly from one another by the input variables included, how the index is constructed using these variables, and thus ultimately, their results. This paper compiles the scores of three well-known remoteness indices for each of 32 localities in Canada’s Northwest Territories. We compare these scores using statistical tests, use k-means clustering to outline new remoteness categories, and assess the strengths and weaknesses of each index. We find that the choice of input variables ultimately determines how remoteness is defined and that different indices should be used to different ends based on this choice. Our findings can guide researchers and policymakers in choosing the most appropriate method to measure remoteness based on objective factors or designing a remoteness index, while also exploring how remoteness can be defined.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".