Bibliographic record
Abstract
Over the last decade or more, geographic information systems (GIS) have proved themselves nimble and potent tools in myriad academic, civic, and political disciplines. A body of scholarship followed GIS on its rise to wider acceptance and adoption, however, that questioned its nature and the way its power was wielded. This scholarship ultimately produced various models for “GIS/2,” an amalgam of GIS's power and the grassroots democratic activity that might have been fostered by it but largely was not. This article revisits going models of GIS/2 and finds them to be so much vapourware compared to recent developments in online geospatial applications. The article argues that for all of the well-intentioned effort put into GIS/2 theory, the most progressive real-world candidate for GIS/2 has been produced only recently, by another rare combination indeed: two Austin, Texas, 20-somethings and the online search monolith Google. The Google Maps mashup, a very twenty-first-century beast born of code from disparate Web applications, exhibits great potential to be a real live GIS/2. Moreover, there is one mashup in particular that, while perhaps not quite mature enough to realistically match 15 years of GIS/2 scholarship, is still possibly the finest working example yet of the ideas and concepts posited therein.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.019 |
| Scholarly communication | 0.016 | 0.022 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".