“Say It Loud, Say It Clear…”: Concerting Solidarity in the Canadian Refugees Welcome Movement (2015–2016)
Bibliographic record
Abstract
The Canadian Refugees Welcome Movement (2015-2016) was one of the most sizeable, visible, and effective instances of collective action in recent Canadian history. It had a nationwide scope and grassroots initiation. It comprised a wide variety of participants and actively employed social media in its constitution. This article reports the results of a multimethod case study that seeks to explain how collective action frames emerged in the context of the Canadian Refugees Welcome Movement; which actors were involved in their articulation; and how they generated a following, collective action and humanitarian and political effect. The focus is on the discursive processes of construction of solidarity across difference as they unfolded in the social media environment. The Facebook event pages calling for rallies in support of Syrian refugees, it argues, served as a discursive space that helped transform the moral shock experienced by members of distinct moral communities into a process of concerting of voices and construction of solidarity and collective action frames across differences.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.041 | 0.026 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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".