Bibliographic record
Abstract
Increasing numbers of activist non-profits and traditionally marginalized peoples are adopting gis as a tool for social change. Its use is scrutinized by academics who worry that gis embodies a mechanism for misrepresentation, diversion, control, and surveillance. This critique has not slowed adoption, and for the past decade researchers have been investigating the use and value of gis in a variety of non-profits and citizens' groups. Many of these new users are calling for a different kind of gis — a gis/2. This gis must be able to represent different measures and visions of place and integrate local knowledge, support cultural and multi-lingual distinctions, and preserve — rather than reduce — friction, disagreement, redundancy, and even error. In this paper I argue that one must "rewire gis" — that is, engage the code and the coding directly — to build a gis/2. A literature review on the use and value of gis in social movements, activist non-profits, and citizens' groups illustrates the limitations of current gis and the necessary ingredients for a more inclusive gis/2. I present four approaches, which are framed materially and discursively. Three approaches modify existing gis to achieve a gis/2. A fourth systems design approach is proposed, which incorporates two innovations in computing science: Unified Modelling Language (uml) and eXtensible Markup Language (xml). This prototype is sensitized to the needs of social movement, nonprofit, and citizens' organizations. These four approaches, used separately or in conjunction, serve as blueprints for further discussions on the rewiring of gis.
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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 | 0.036 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.006 | 0.033 |
| Scholarly communication | 0.015 | 0.028 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".