Tweeting and Retweeting for Fight for $15: Unions as Dinosaur Opinion Leaders?
Bibliographic record
Abstract
Abstract Advocacy campaigns are central to unions’ efforts to impact labour rights beyond unionized workplaces. Social media and on‐the‐ground campaign dynamics are intimately related. Thus, if unions can become leaders on social media, they could have more impact on campaign framing and mobilizing. Drawing on primary data and applying a sequential mixed method, we analyse unions’ ability to emerge as opinion leaders in Twitter dialogues on the Fight for $15 (FF$15) campaign. We track FF$15‐related activities of Twitter profiles over seven months and compare union actions to those of others along three dimensions: level of activity, prevalence of tweeting versus retweeting and endorsement within FF$15 community and in the Twitter universe. Regression results show unions prefer advancing their own ideas over supporting those of others, and their messages are more endorsed than others’ messages in the Twitter universe. In‐depth interviews and a focus group reveal that while their actions are slow and conservative, unions can count on internal support and institutional reputation to gain leadership. The article concludes by noting the implications of the findings for unions’ strategies to become opinion leaders on social media.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".