Toward an Understanding of the Spatiality of Social Movements: Labor Organizing at a Private University in Los Angeles
Bibliographic record
Abstract
In this paper, we examine a labor struggle between predominantly Latino service workers and the University of Southern California, the largest private employer in the City of Los Angeles. This struggle is part of a broader revival of the American labor movement, as some unions return to mass action and community-labor alliances. The re-emergence of labor as a social movement allows us to ask new questions about power and resistance. In particular, we maintain that a full understanding of the political potential of social movements requires recognition of their inherently spatial nature. Drawing on the recent spatial turn in social theory, we argue that social movement scholarship can benefit from attention to space as an active dimension of movement organizing. In an ethnography of the USC case, we show how a coalition of workers, students and community members used tactics of spatial transgression on, around, and beyond campus. At the same time, coalition members linked the labor conflict to social and spatial inequalities between the university and surrounding neighborhoods, and to citywide movements for living wages and job security. Through these actions, the coalition undermined a commonsense understanding of USC as a benevolent employer and good neighbor, and challenged the university's move to gain flexibility through sub-contracting. While we are constrained in our ability to generalize from the USC case, our analysis suggests that further attention to the spatiality of such struggles can enrich social movements scholarship.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.018 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".