When digital capitalism takes (on) the neighbourhood: data activism meets place-based collective action
Bibliographic record
Abstract
Recent social movement scholarship has highlighted the instrumental and material roles played by digital technologies in supporting collective action and new cultures of organizing. This research seldom considers the symbolic role of digital technologies and data. However, the extraction and exploitation of data, described as data colonialism, facilitate novel opportunities for capitalist expansion in the everyday. Extensive data appropriation and commodification have led to a growing interest in data activism which challenges dominant data politics. Focusing on the intersection of data activism and local organising of collective action, this article examines two cases of how Big Tech disrupts everyday life and becomes a grievance used for mobilisation. With these cases, we illustrate how protest campaigns react to Google’s aim to colonise both digital and physical spheres of life. The first case concerns the creation of a Google Campus in Berlin, while the second focuses on the Sidewalk Toronto urban development project led by the Google subsidiary, SideWalk Labs. Both projects were met with resistance comprising elements of data activism, mobilised as the Fuck Off Google and #blocksidewalk campaigns. Beyond rallying local discontent around impending gentrification and increased housing prices, the campaigns raised awareness about digital giants’ unethical data practices and underscored alternative human-centric technologies and data politics. Employing frame analysis, we elaborate on the intersecting dynamics of traditional, community-based grassroots mobilisation and data activism against Googlization and explore the potentialities and limitations toward contextualising collective action for (rather than in) the digital age.
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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 | 0.004 | 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 | 0.014 | 0.042 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.001 | 0.017 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".