Practitioner Perspectives on Arctic Marine Mammals in Environmental News Reporting
Bibliographic record
Abstract
An advance online version of this article was first published September 2024.The conservation and environmental policy literature suggests that featuring charismatic megafauna or flagship species—large animals with which humans are fascinated—in environmental communications helps to raise awareness and create public and political support for the protection of ecosystems or species. While a considerable body of literature is dedicated to such species, scholars have paid comparatively little attention to the human practitioners creating these flagship-based communications. To fill the literature gap, this article draws on agenda-setting theory and empirical evidence concerning the Arctic—the fastest-warming region on Earth—and its charismatic marine mammals. Through interviews and informal conversations with journalists, researchers, and policy-makers, the study asks 1) why these practitioners contribute to flagship-based news coverage, 2) how they interact with other practitioners in this process, and 3) how they view the content of the news coverage. The article highlights practitioners’ motivation to harness human fascination with Arctic marine mammals to draw attention to broader environmental issues, most notably the climate crisis. At the same time, the article outlines trends in flagship-based news coverage that practitioners perceived as problematic, including the representation of polar bears, human perspectives, and different systems of knowledge. Practitioners also discussed challenges hindering accurate and nuanced Arctic environmental news reporting, including budget, personnel, and time constraints. Through its analysis of first-hand practitioner accounts, the article provides valuable insights and practical information for researchers, journalists, and policy-makers seeking to engage with and improve environmental news reporting concerning Arctic marine mammals, as well as related conservation efforts.
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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 | 0.088 | 0.158 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.020 | 0.018 |
| Scholarly communication | 0.021 | 0.019 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".