Inclusion of Latino-oriented local businesses in popular online maps
Bibliographic record
Abstract
Entrepreneurship in the Latino community is transforming the business landscapes of small and medium-sized cities throughout agricultural regions of the United States. These new businesses offer their owners and employees an alternative to farm or industrial work, while creating jobs, revitalizing often-vacant parts of town, and offering a sense of place and familiarity to recent immigrants and their families. This study examines to what degree popular online maps are likewise transforming to include these Latino-oriented local businesses. We visited strategically-selected commercial areas of four cities with relatively high Latino populations in the Inland Northwest region, recorded all operational businesses, then compared this inventory with businesses symbolized on Google Maps, Apple Maps, Bing Maps, and OpenStreetMap. We also studied the activity history of contributors who added Latino-oriented local businesses to OpenStreetMap. We found that Latino-oriented local businesses appeared in significantly fewer map platforms than other businesses. Additionally, national chain businesses appeared in significantly more map platforms than local businesses, and areas with relatively high Latino populations saw significantly less mapping of businesses than other areas. OpenStreetMap had low inclusion of Latino-oriented local businesses. We offer possibilities for future research and ways to improve the rate of mapping of these businesses. We also describe how all field notes from this project were added to OpenStreetMap following the study.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".