Bibliographic record
Abstract
Augmented reality is an emerging technology that overlays digital images onto depictions of the real-world using a hand-held device. Augmented properties, where these real and virtual spaces intersect, raise interesting norms conflicts. While norms literature has emphasized the importance of communities, the significance of place has not been as strongly emphasized as a factor. To illustrate the place-based nature of norms, this paper examines norms conflicts between real property and technology communities that occurred when Pokemon Go, a gaming application that uses augmented reality, was played at memorial sites. We discuss Pokemon Go as an example of augmented reality and detail how the technology creates augmented properties. The paper analyses in detail the norms conflicts that arose when Pokemon Go players visited memorial sites, such as cemeteries, war museums, and monuments, which attracted media attention and public condemnation. Notably, the strident criticisms against playing the game at places of remembrance could not be explained in terms of real property depletion, trespassing, or overcrowding. Instead, we argue that the conflicts may be explained based on the place-based nature of norms, the still emerging norms around the use of augmented reality technology, and the uncertainty around the norms for new places of augmented property. Significantly, although augmented reality as a technology is spatially precise, it is not sensitive to place and does not incorporate place-based norms in its architecture. Building on these insights, we recommend using “zoning” mechanisms from property law and technology to mediate these place-based norms conflicts that may continue to occur as the use of augmented reality affects real property.
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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 | 0.014 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.037 |
| Scholarly communication | 0.016 | 0.022 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".