Bad Boundaries: Geofences and the Intimacies of Location Data
Bibliographic record
Abstract
Locational data are a key part of platform function. They organize people and environments according to position and proximity. One technique through which platforms collect and circulate locational data is through geofences—virtual perimeters established around target locations that mark who and what crosses their thresholds. Applying the lens of data intimacies, the authors look at two applications of geofences in the United States: (a) to target abortion seekers through geofencing brokers like CellHawk that extrapolate and sell Google Maps locational data and (b) to accelerate the tenant eviction process through platforms such as CIVVL, a so-called property preservation platform. Through the examples of CellHawk and CIVVL, this article argues that geofences enable platforms to organize space and make claims on the body via location data. Geofencing is a practice that exploits the intimacy of locational data not simply by accessing private data but also by using intimate data to index the body in relation to risk and safety and property and trespass. Geofencing is a mechanism through which platforms police and patrol space, reifying unequal terms of autonomy and access.
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.
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 | 0.002 |
| 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 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| 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".