Actors and justifications in media debates on Arctic climate change in Finland and Canada: A network approach
Bibliographic record
Abstract
In this paper, we examine the centrality of policy actors and moral justifications in media debates on Arctic climate change in Finland and Canada from 2011–2015. We take a network approach on the media debates by analysing relations between the actors and justifications, using discourse network analysis on a dataset of 745 statements from four newspapers. We find that in both countries, governments and universities are the most central actors, whereas business actors are the least central. Justifications that value environmental sustainability and scientific knowledge are most central and used across actor types. However, ecological justifications are sometimes in conflict with market justifications. Government actors emphasize new economic possibilities in the Arctic whereas environmental organizations demand greater protection of the vulnerable Arctic. Ecological justifications and justifications that value international cooperation are more central in the Finnish debate, whereas justifications valuing sustainability and science, as well as those valuing national sovereignty, are more central in the Canadian debate. We conclude that in addition to the centrality of specific policy actors in media debates, the use of different types of moral justifications also reflects political power in the media sphere.
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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 | 0.006 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.016 | 0.018 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".