Bibliographic record
Abstract
Abstract In the 20 years that have passed since the fabled Friday Harbor meetings of November 1993, where GIS practitioners and critical human geographers agreed to a cease‐fire, the GIS & Society agenda has been reflected upon, pushed forward, and diffracted in few (but intellectually significant) arenas. Critical, participatory, public participation, and feminist GIS have given way more recently to qualitative GIS, GIS and non‐representational theory, and the spatial digital humanities. Traveling at the margins of these efforts has been a kind of social history of mapping and GIS. And while GIScience has been conversant and compatible with many of these permutations in the GIS & Society agenda, a social history of mapping and GIS (as signaled most directly by John Pickles in ) has perhaps the least potential for tinkering with GIScience practice (see conversation between Agnieszka Leszczynski and Jeremy Crampton in 2009). Perhaps this disconnect is growing, as can be witnessed in the feverish emergence of a “big data” analytics/visualization perspective within the contemporary GISciences (alongside the growth of funding paths around cyberinfrastructure). What then is the relevance and role of a social history of GIS for GIScience practice? In this viewpoint, I sketch and reflect upon a diversity of efforts that address this question.
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 | 0.017 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.030 | 0.149 |
| Scholarly communication | 0.030 | 0.022 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 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".