Navigating digital geographies: Black boxes, geospatial narratives, and the art of constructing location data
Bibliographic record
Abstract
Smartphone location data are often treated as objective and self-evident—but it is neither. This article opens the black box of how location is constructed on the phone and in the cloud, arguing that these processes are foundational to digital geography and central to how its infrastructures take shape. Drawing on an original experiment conducted in Kingston, Ontario and Baltimore, Maryland, we reverse-engineer and document the different methods of producing location data in Android smartphones. In doing so, we reveal three intertwined, overlapping, and contested geospatial narratives: raw GNSS location data, Google’s computed location data, and the human narrative of embodied experiences. We analyze the frictions and contradictions among these narratives to demonstrate how location data are not simply measured, but actively produced through assemblages of surveillance, infrastructural power, and capitalist extraction. Against dominant portrayals of location as a neutral technical fact, our findings show that Google’s location services depend on off-phone processing, structured by opaque systems designed for control and profit. We call for a critical reorientation in how digital geographers engage with location technologies—not as passive tools, but as politically charged systems that mediate and monetize everyday life.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.048 |
| Scholarly communication | 0.013 | 0.018 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".