The many voices of business: Framing the Keystone pipeline in US and Canadian news
Bibliographic record
Abstract
Corporations rarely enter political battles alone. They have long partnered with trade associations to articulate industry views, and more recently have begun routinely creating their own activist organizations to act as allies. Amid this turn toward grassroots corporate organizing, how is the voice – or perhaps voices – of business articulated in the news? Using the case study of coverage of the Keystone bitumen pipeline, I offer a framing analysis of 480 news items from six outlets in the United States and Canada, showing which voices and frames dominate the debate. My data demonstrate that while corporations have a robust voice in news, trade associations participate only sparingly, and corporately funded grassroots campaigns are almost wholly omitted. Furthermore, key silences characterize corporations’ mediated voice, with companies neglecting to comment on issues such as climate change; anti-pipeline activists, meanwhile, maintain their own forms of strategic silence. Proponents and detractors alike promote their ‘owned issues’, offering discourse more akin to a shouting match than a debate.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.030 | 0.018 |
| Scholarly communication | 0.020 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| 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".