Using Cell-phone Mobility Data to Study Voter Turnout
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Studies of voting behavior in some settings may be hampered by poor data availability or unsuitably large units of aggregation for reported turnout. We propose and demonstrate a practical big-data solution to these kinds of challenges, using fine-grained cell-phone mobility data on millions of GPS locations for more than 300,000 eligible voters in Tokyo. Our approach uses the geolocations of polling stations, combined with GPS data points recorded on election day and a reference day, to measure patterns in individual-level (but anonymized) voting behavior. We first test the validity of the measure by comparing it to official aggregated data on turnout, and then illustrate its substantive utility with an application exploring the well-known relationship between turnout decisions and the cost of voting, proxied by the distance between a voter’s residence and the polling station. Finally, we discuss the potential limitations of the approach and provide step-by-step instructions for other researchers.
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.
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 | 0.000 |
| 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) | 0.001 | 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 it