Using a Social-Ethical Framework to Evaluate Location-Based Services in an Internet of Things World
Bibliographic record
Abstract
The idea for an Internet of Things has matured since its inception as a concept in 1999. People today speak openly of a Web of Things and People, and even more broadly of an Internet of Everything. As our relationships become more and more complex and enmeshed, through the use of advanced technologies, we have pondered on ways to simplify flows of communications, to collect meaningful data, and use them to make timely decisions with respect to optimisation and efficiency. At their core, these flows of communications are pathways to registers of interaction, and tell the intricate story of outputs at various units of analysis- things, vehicles, animals, people, organisations, industries, even governments. In this trend toward evidence-based enquiry, data is the enabling force driving the growth of IoT infrastructure. This paper uses the case of location-based services, which are integral to IoT approaches, to demonstrate that new technologies are complex in their effects on society. Fundamental to IoT is the spatial element, and through this capability, the tracking and monitoring of everything, from the smallest nut and bolt, to the largest shipping liner to the mapping of planet earth, and from the whereabouts of the minor to that of the prime minister. How this information is stored, who has access, and what they will do with it, is arguable depending on the stated answers. In this case study of location-based services we concentrate on control and trust, two overarching themes that have been very much neglected, and use the outcomes of this research to inform the development of a socio-ethical conceptual framework that can be applied to minimise the unintended negative consequences of advanced technologies. We posit it is not enough to claim objectivity through information ethics approaches alone, and present instead a socio-ethical impact framework. Sociality therefore binds together that higher ideal of praxis where the living thing (e.g. human) is the central and most valued actor of a system.
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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 | 0.064 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.010 | 0.058 |
| Scholarly communication | 0.017 | 0.018 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".