Sovereigns, Spooks, and Hackers: An Early History of Google Geo Services and Map Mashups
Bibliographic record
Abstract
Google geo services, such as Google Maps and Google Earth, offer popular and powerful geographic ways of seeing the world. This is a social history of the cartographic visions at work in Google geo services and related geoweb applications/map mashups. These geographic technologies are the result of shifting configurations of power that include state programs, private corporations, and small-scale tinkerer/hackers. Through these shifts, two particular geographic ways of seeing develop and come together: multi-scalar hyperlocal views and aerial imagery. These visual geographic knowledges were used together for military purposes during the Cold War and combined using 1990s video game software. Google popularized hyperlocal and aerial imagery ways of seeing by applying them in its targeted advertising-based business strategy. Hackers re-engineered Google Maps to create map mashups (geoweb applications), and Google executives chose to incorporate those applications into the company's strategy. Constructing this conditional history with its changing knowledges and variety of actors indicates how sovereign power and, later, capitalism had fundamental roles in forging ways of seeing with maps on the Web. Google's maps today have their own limits. They are highly individualized and often consumption-oriented but may prompt new kinds of mappings in the future.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.007 | 0.028 |
| Scholarly communication | 0.012 | 0.024 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".