What's Really at Stake? Insights from Nova Scotia Teachers’ Use of Social Media during Collective Bargaining
Bibliographic record
Abstract
This study uses the case of the 2015-17 contract negotiations between the Government of Nova Scotia (Canada) and the Nova Scotia Teachers’ Union to understand (1) how the Government, the union, and individual educators framed the issues at stake in negotiations, and (2) how those perspectives were represented in traditional and social media. To answer these questions, I conducted two unique content analyses of news releases, social media posts, and traditional digital media over the course of negotiations. Results show that educators used social media to frame their work explicitly in terms of caring labour and to communicate with specificity and urgency the toll of their working conditions on their ability to meet the needs of their students and maintain their own well-being. However, active educators’ voices were rarely included in traditional media. These findings show that using social media does not guarantee that teachers’ perspectives will influence the broader public discourse. Moreover, they suggest that to the extent that care frames are employed in teachers’ strikes, it may be important for unions to develop an official campaign—in collaboration with rank-and-file educators—that is centred around care.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.010 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".