(Un)Managing Emotions at the Forefront: Stories from Shoreham Picket Line
Bibliographic record
Abstract
In this article, I reflect on my experience as an active rank and file member of CUPE 3903, the union representing contract faculty and graduate students at York University in Toronto, Ontario, during the 2018 York University Strike, where I volunteered as a front-line communicator, or “car talker”. Drawing on these experiences, I reflect on the ways in which picketers generally try to (un)manage the emotions of drivers passing through the picket line. My analysis is focused on a particular venue - the Shoreham picket line located at the southwest entrance of the university, and centers around my personal interactions with the drivers crossing the picket line during the morning hours from March 2018 to May 2018. My analysis aims to open up space to discuss the largely overlooked role that the emotions of the public play in shaping the picket line experience. In particular, I provide a multi-directional analysis of the encounters that occurred between the picketers and the general public at the Shoreham picket line during the 2018 strike, highlighting the multiplicity of variables, such as the environment, the pre-existing beliefs of the participants, and expressions of collective anger, which informed these encounters. In doing this, I illuminate the complexity of the intertwined relationship between emotional and cognitive framing, thereby providing a more comprehensive model for understanding the role that emotions play in social movement organizing.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.034 | 0.020 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".