Towards a New Flashmob Unionism: The Case of the<i>Fight for 15</i>Movement
Bibliographic record
Abstract
Abstract Unionism renewal has been described as a hybridization process between ‘old’ and ‘new’ logics. Understanding how these two potentially conflicting logics might be combined, however, has so far received little attention. Through the study of the Fight for 15 (FF15) movement, we investigate how the old ‘collectivist’ logic of action‐oriented unions and the new ‘connectivist’ logic are being hybridized. To do so, we develop a mixed‐methods approach that combines interviews with Twitter data. We evidence three mechanisms through which the collectivist and connectivist logics are being hybridized, namely, imbrication, camouflage and cumulation. We suggest to name ‘flashmob unionism’ the hybrid logic of FF15, characterized by apparently spontaneous mobilizations, a loosely co‐ordinated organization, a personalized communication and online virality.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.022 | 0.021 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".