Bridging across difference in contemporary (urban) social movements: territory as a catalyst
Bibliographic record
Abstract
This editorial introduces a collection of papers that contributes to two strands of debates: the transformation of urban- and place-based social mobilizations; and the relationships and collaboration between highly diverse groups coexisting in a particular place. The introduction develops the three sets of questions that underpinned the collection (which take us to Istanbul, Madrid, Berlin and the territories surrounding Montreal and Boston): (1) Which kind of urban or territorial issues, processes or threats act as trigger/catalyst for the emergence of new coalitions between highly diverse individuals, groups or existing movements? How does urban space, or the ‘territory’ more broadly, act as a politicizing force in the process of formation of such highly diverse mobilizations? (2) Who are the actors of those diverse mobilizations? To what extent do they span across class, migration status, ethnic and other forms of social divisions, and point to cooperation between the ‘materially dispossessed’ and ‘culturally disenfranchised’? (3) How do heterogeneous groups bridge across their differences in the process of mobilization and activism, and which challenges do they face in so doing? Which repertoires of contention and modes of action do the diverse components of the mobilization bring with them? How complementary or conflicting are they?
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.030 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".