Framing Environmental Dilemmas: The Ethical Positioning of the Seal Hunt In Two Canadian Newspapers
Bibliographic record
Abstract
The aim of this study is to investigate how moral issues surrounding one of the lengthiest environmental conflicts – the Canadian seal hunt controversy - were communicated by two Canadian mainstream newspapers: the national Globe and Mail, and the Newfoundland-based The Telegram in 2009, the year in which the European Union banned the import of all seal products on the basis of a moral standard relating to the welfare of animals. At a general level, the purpose of this work is to examine how the news media construe and convey environmental ethical positions when dealing with complicated environmental issues. To this end, this thesis draws from media framing theory to implement a qualitative linguistic analysis of the 99 news articles to analyze how seals and sealers – the two main subjects of moral worth in this controversy – were framed in the two newspapers. The analysis found that seals were predominantly framed in accordance with their perceived social and economic benefit, largely overlooking animal welfare considerations. Sealers, on the other hand, tended to be portrayed as people of moral rectitude and brave seafarers, with a concomitant onus placed on the cultural and economic importance of sealing for Northerners. The findings corroborate claims that our perceptions of animal species, especially those which are considered wildlife, and the type of our relationship with them vary in accordance with human utility. At the same time, these perceptions are influenced by the social and cultural aspects of humans' relationship with the environment that may trump considerations of animal welfare and compassion toward sentient animals. Seen in the perspective of environmental ethics debate, the seal hunt controversy reveals the current lack of consensus on determining the most sound ethical principle in order to ensure our treatment of the environment is morally consistent. As the seal hunt controversy is not a standalone phenomenon of the protest based in animal welfare considerations, this thesis can be of value for the future research of comparable environmental controversies. Reconciling antagonistic environmental ethics is important for environmental policy-making and management, in order to ensure a greater and more productive stakeholder participation in solving environmental issues more effectively, while at the same realizing our moral obligations towards the animal world and the rest of the nature.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".