Opportunistic natural experiments using digital telemetry: a transit disruption case study
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
In the past decade society has entered a technological period characterized by handheld computing that supports input and processing from numerous sensors. Today’s mobile phones offer the ability to integrate input from sensors monitoring various external and internal sources (e.g., accelerometer, magnetometer, microphone, GPS, wireless Internet, and Bluetooth). Furthermore, these raw inputs can be integrated and processed in ways that can offer novel representations of human behaviour. As a result, new opportunities to examine and better understand human spatial behaviour are available; one such application is the constant monitoring of a group of people over an extended period of time. Such a research setting lends itself to natural experiments that emerge as a result of regular and on-going observations. We report here on the observation of a natural experiment that took place in the context of a month-long monitoring study of 28 participants using mobile phone-based ubiquitous sensor monitoring. The implications for public health and transportation planning are discussed.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| 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 it