At Your Side, On Your Side: United Nurses of Alberta communications framing
Bibliographic record
Abstract
This case study focuses on the United Nurses of Alberta, the union representing registered nurses in the province of Alberta, Canada; this explores United Nurses of Alberta's communication strategies. Drawing on the collective action frames previously identified in United Nurses of Alberta's social media and newsletters from 2010 to 2015, which frames nurses as unique healthcare providers and advocates, this study leverages insights from 23 interviews conducted with the United Nurses of Alberta staff, highly involved members, and general members from 2016 to 2017. The article explores the motivations and tensions around the framing of nurses and the union. The findings indicate that the United Nurses of Alberta could enhance its communications by better aligning with members’ current struggles through various collective action frame bridging and extensions. The research also suggests the potential benefits of United Nurses of Alberta shifting away from collective action frames rooted in self-sacrifice. Furthermore, this case study provides recommendations for communication strategies that could strengthen member engagement and involvement within their unions.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.034 | 0.012 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".