Spatial Awareness in Locative Media Projects
Bibliographic record
Abstract
This MRP focuses on the different applications that location technologies take part of such as, free based navigational systems like Google Maps, and Locative Media art projects; and analyzes their impact on people and their experience of space. By determining that Google Maps and Locative Media are on different sides of the spectrum of location technologies, I suggest that they are developing different territorial discourses through the use of digital mapping. I suggest that Google Maps is developing a territorialization of space by modifying the way in which the body recognizes space, by creating an image of the world that is designed towards a single user—the Google user—and by imposing itself as a map that is able to represent space. Using a theoretical approach towards understanding these effects, I then analyze three Locative Media project that challenge these ideas, they are: Cary Peppermint’s project, “Indeterminate Hikes,”“Megafone/Montreal in*accessible” by Antoni Abad, and “Amsterdam Real Time” by the Waag Society.
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".