Global Journalism in Decision-Making Moments: A Case Study of Canadian and American Television Coverage of the 2009 United Nations Framework Convention on Climate Change in Copenhagen
Bibliographic record
Abstract
Climate change is a phenomenon with global causes but local effects, and thus global climate change decision-making moments provide ideal opportunities to examine how local and global discourses work together—or do not—through global journalism. This case study investigates the globally focused vs. culturally bound frames used in television news coverage, in Canada and the USA, of the 2009 United Nations Climate Change Conference in Copenhagen. Initial quantitative findings that Canadian media used many more culturally bound sources than did American media contradict the past findings and suggest Canadian media engaged less in producing global journalism than did American media. A follow-up qualitative analysis not only found more global framing in the American stories, but also concluded that global sources did not necessarily create global journalism; instead, a global orientation is required.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.038 | 0.012 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".