Another Politics Is Possible: Neogeographies, Visual Spatial Tactics, and Political Formation
Bibliographic record
Abstract
Neogeography – the use of interactive online mapping technologies, often by laypersons or grassroots groups – continues its rapid growth, as do debates about its implications for spatial data and map quality, public spatial literacy, and the digital divide. Ongoing efforts to understand whether and how neogeography might enable the participation, influence, and agency of less powerful social actors require greater attention to theorizing neogeography politics. Existing work, tacitly or explicitly, tends to theorize these politics in ways that align with Michel de Certeau's notion of “strategy” or its conceptual partner, “tactics.” We argue that a neogeography politics conceived as “strategy” has inherent limits and that the political significance of neogeography “tactics” is even more foundational than has been understood thus far. Recent work has shown neogeography to be a powerful site of political action or engagement, but our evidence suggests further that visual spatial tactics in neogeography are also key sites of political formation. Neogeography tactics are significant not just as a site of resistance or political action by less powerful actors but also as practices that contribute to the formation of political subjects, mobilized social groups, and shared knowledge. Recognizing neogeography as a site of political formation paves the way toward realizing its broader potential in the development and practice of a critical spatial citizenship. We develop these arguments from a three-year neogeography project conducted with young teens.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.042 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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".