PERILS OF PLACE: GEOFENCES AND PREDATORY PLATFORM INTIMACIES
Bibliographic record
Abstract
For many, mapping platforms are enmeshed in everyday experiences. We navigate, locate, and move through the world with the help of their locative affordances. Consequently, these platforms have an intimate awareness of our movements and location history, and this information is valuable for advertisers. One way that platforms can track and share this information is through geofences, commonly used by companies to send targeted advertisements directly to platforms. Geofences are virtual perimeters established around target locations that act as a digital tripwire, marking who and what crosses its threshold. Digital mapping platforms like Google Maps broker this location data to third-parties (Bui, Chang, & McIlwain, 2022). This paper examines two applications of geofences as intermediaries of locational data. The first is the use of geofences by the property platform, CIVVL, that applies geofences to facilitate and accelerate the tenant eviction process. The second is Hawk Analytics, a locational data broker that geofences abortion clinics and sells the locational data from the clinic’s clients to anti-choice organizations, in jurisdictions of the United States where such healthcare is illegal. In our analysis of locational data, we apply the concept of platform intimacies (Rambukkana and de Verteuil, 2021; Ley, & Rambukkana, 2021) to understand the techniques through which geofences access private locational details. This paper examines the spatial relations the geofence enforces and how this often-unregulated informational infrastructure can be applied to weaponize location data. We argue that the geofence enables an extractive relationship with intimate platform knowledge while it enforces hegemonic notions of trespass and belonging.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".