The value of agenda-setting in media relations: Examining how the Business Community Anti-Poverty Initiative used agenda-setting to leverage its role as a policy actor in New Brunswick’s 2014 provincial election
Bibliographic record
Abstract
This case study explores how and to what extent the Business Community Anti‑Poverty Initiative (BCAPI), in Saint John, New Brunswick, used agenda-setting in media relations leading up to the 2014 provincial election. Research, including interviews with three people involved with BCAPI, a literature review, an analysis of media coverage, and a review of BCAPI’s strategy, indicated that BCAPI proactively engaged the provincial daily print newspaper, the Telegraph-Journal, to help influence party platforms and public interest on poverty reduction and ultimately received $300,000 in provincial funding for its initiatives. Keywords: media relations, agenda-setting, policy actors, framing, elections
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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 | 0.014 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.031 | 0.026 |
| Scholarly communication | 0.017 | 0.006 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| 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".