Wasted? The Downstream Effects of Social Movement–Backed Occupations
Bibliographic record
Abstract
Studies examining the impact of social movements on organizations have focused primarily on what leads to initial concessions in response to movement targeting. A key remaining question is what comes next, or how do movement priorities become institutionalized within organizations and across fields via downstream processes? We argue that central actors in these downstream efforts are members of occupations that have been created out of movement pressure on organizations. In this study, we examine the longitudinal evolution of a movement-backed occupation: recycling coordinators in higher education. By conducting historical, processual analyses of 25 years of online conversations among over 1,000 recycling coordinators, we identify three key tensions they faced in trying to embed practices and an ethos from the environmental movement and in trying to progress their organizations toward evolving movement concerns (from recycling to sustainability). We uncover how the coordinators navigated these tensions, finding that while they succeeded in institutionalizing recycling and expanding their organizations toward a new wave of movement concerns regarding sustainability, their occupation nonetheless experienced demise. Our findings set the foundation for future research on the downstream efforts and occupational actors that are vital for institutionalizing movement demands.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".