Incorporating Trip-Chaining to Measuring Canadians’ Access to Cash
Bibliographic record
Abstract
Household mobility data can improve our measurement of access to cash. The existing literature typically assumes that households visit their nearest ABMs or financial institution branches from their homes, without combining cash withdrawals with other activities (i.e., on their way to shopping). However, the typical approach neglects two realistic features: The first is that, due to spatial agglomeration, cash access points could be co-located with popular points of interest, such as retail service centers; and, second, households could combine multiple trips, via trip-chaining, to reduce travel costs. Our paper employs smartphone data to construct an improved cash access metric by accounting for both spatial agglomeration and households’ travel patterns. We find that incorporating trip-chaining into the travel metric could show that travel costs are from 15% to 25% less than not incorporating trip-chaining and that the biggest decrease is driven by rural residents.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".