Counter (Mapping) Actions: Mapping as Militant Research
Bibliographic record
Abstract
We, the Counter Cartographies Collective (3Cs), propose a specific form of counter-mapping, autonomous cartography, to understand and intervene in the processes at our university, the University of North Carolina at Chapel Hill. As autonomous, militant research, this mapping aims to foster cooperation among researchers and participants to practically intervene in real problems without attempting to marshal state or administrative power. Our experience shows that autonomous cartography helps produce new, alternative practices, knowledges and subjects at our university and others. Even as we draw on critical cartography and other cases of counter-mapping, autonomous cartography constitutes a distinct form of counter-mapping through our combination of autonomous theory, militant research and mapping. In this paper, we explore constitutive influences on 3Cs and our own militant countermapping experiences. We begin with a review of the theoretical basis of autonomous cartography as a form of critical cartography and counter-mapping. Next, we introduce the key concepts and practices of autonomous politics and militant research through the examples of Colectivo Situaciones, Precarias a la Deriva and Hackitectura. In the second half of the paper, we review 3Cs’ founding conditions and two of our maps. Finally, we conclude by examining the methods and impacts of our mappings.
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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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.016 | 0.054 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".