GEOLOCATION: LOCATING TRUST IN DIGITAL PLATFORMS AND ECONOMIES
Bibliographic record
Abstract
Digital location—or geolocation—is a fixture of digital platforms and economies, figuring as an organizational logic for content and user experience (e.g., map-based interfaces), native technical affordance (e.g., locational functionalities of GPS-enabled smartphones), and core enabling agent behind the rise of ‘disruptive’ platform enterprises (e.g., Uber, Deliveroo, and Waze all rely on geolocation for service delivery). Despite its inextricability from contemporary media, data productions, and digital practices, geolocation’s role in fostering mis/trust in digital systems has to date been unaddressed. The papers in this panel identify and theorize the ways in which geolocation functions as simultaneously a key ‘technology of trust’ (sociotechnical agent through which mis/trust is bred and/or secured in digital ecosystems), and a social relation of trustworthiness in digital platforms and economies, exploring the role geolocation plays form the perspectives of both securing trust and breeding mistrust in digital systems.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".