Bibliographic record
Abstract
Counter‐mapping is a combination of critical ideas and practices for social change that offers a productive and promising approach for grassroots data science initiatives. Current information technologies collect, store, and analyze data with new degrees of size, speed, heterogeneity, and detail. While much work utilizing data science technologies is dedicated to generating profit or to national security, some data science projects explicitly attempt to facilitate new social relations, though with inconsistent results and consequences. This paper reviews counter‐mapping's particular combination of theory and practice as a potential point of reference for such initiatives. Counter‐mapping takes the tools of institutional map‐making at government agencies and corporations and applies them in situated, bottom‐up ways. Moreover, counter‐mapping's multiple theoretical approaches and polyglot practices offer a variety of inspirations and avenues for future work in identifying and realizing alternative, ideally better, possibilities. This paper defines counter‐mapping; outlines its multiple theorizations; briefly describes three relevant case studies, The Detroit Geographical Expedition and Institute, Mapping Police Violence, and the Counter‐Cartographies Collective; and concludes with a few hard‐learned considerations from counter‐mapping that are directly pertinent for data‐oriented projects focused on change.
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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 | 0.089 | 0.143 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.017 |
| Science and technology studies | 0.012 | 0.073 |
| Scholarly communication | 0.034 | 0.047 |
| Open science | 0.006 | 0.024 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.011 | 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".