Bibliographic record
Abstract
By changing the connectivity between people across the globe, the rise of social media has shifted the resources and capacities of political activists, opening up new horizons for social movements.Many of the labour movement's renewal goals-such as improving equity within unions, adopting more inclusive grassroots organizing, and reaching out to a precarious, fragmented workforce-seem to line up with this open potential of social media.However, existing research on unions' use of social media suggests the goals and practice don't align, arguing that unions tend to use social media in a unidirectional, centralized way.To explore this discord, this study investigates the use of social media by four of Canada's largest labour organizations-the Canadian Union of Public Employees (CUPE), the United Food and Commercial Workers Canada (UFCW), Unifor Canada, and the Canadian Labour Congress (CLC).In comparing the strategies expressed in interviews with key communications staff and the practices evident in the unions' social media output with the stated principles and goals of the organizations as a whole, a number of tensions between labour communications and social media platforms become evident.On the one hand, unions struggle in maintaining centrally controlled messaging in a context that favours open, pluralistic communications.On the other hand, while social media has become an essential arena for public discourse, it's one where the connectivity it offers is manipulated by algorithms created in the interest of private profit.There is a clear and compelling need to strengthen Canadian unions in order to address growing economic inequality, and by filling gaps in the research of unions' current communication strategies, this study can contribute to efforts to formulate some best practices for using social media as a democratic tool in the Canadian labour movement.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.012 |
| Science and technology studies | 0.024 | 0.007 |
| Scholarly communication | 0.020 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.050 | 0.004 |
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".