Power in numbers/Power and numbers: Gentle data activism as strategic collaboration
Bibliographic record
Abstract
Abstract This short piece responds to a call to unpack the notion of gentle geographies conceptually and methodologically. This response considers gentleness in the context of ‘data activism,’ which describes actions to resist the harmful effects of surveillance by corporate and state actors, as well as those that harness the potential of data to achieve grassroots social and political goals. Regarding the latter form, this piece considers the potential of an explicitly gentle form of data activism in which collaboration with policy actors is a central strategy, which contrasts it with a longer history of oppositional, or even ‘militant’ forms of data activism. Gentleness is characterised here as a careful, consciously moderated, and above all, strategic mode of action; it can be deployed to advance specific activist goals and to exploit the growing allure of data in urban planning and governance circles. Through examples from Vancouver, Canada and Cape Town and Johannesburg, South Africa, and by engaging with recent work on the connections between data and action, gentle data activism is put forward as a mode of action that merges power in numbers (in the sense of collaboration and diverse perspectives, but not in the sense of data as capable of action on its own) with power and numbers (an understanding of data's actionability as being contingent on a wider set of forces). This in/and distinction foregrounds a need for those engaged in data activism to carefully consider whether their actions are intended to achieve outcomes that are instrumental (achieving tangible changes) and/or normative (challenging power asymmetries). Gentle modes of action may be highly appropriate for goals such as influencing policies that affect marginalised communities, but gentleness may not be suitable for challenging the injustices at the root of marginalisation.
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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 | 0.018 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.091 |
| Scholarly communication | 0.020 | 0.020 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".