From human invaders to problem bears: A media content analysis of grizzly bear conservation
Bibliographic record
Abstract
Abstract Across their North American range, grizzly bears ( Ursus arctos ) occupy a special place in human imagination, as icons of nature's rugged and raw power, to representations of safety risks and economic costs of living with carnivores. Different bear representations can also be found across news media, from controversial and sensational descriptions of attacks, tragic events and conflicts, to scientific accounts of conservation research. News media certainly has the power to pique curiosity, spark debate, or elicit emotional responses through framing and repetition of content. In turn, news stories can influence how people might interpret and internalize information about grizzly bears. Using media content analysis, we examined newsprint stories on grizzly bears across their North American range between 2000 and 2016, to understand message framing and attention cycle, as well as attitudinal expression and representative anecdote conveyed to the readership. We found that human–bear conflict stories are over‐reported compared to other narratives, where a single incidence garners more attention than a story about new scientific findings. We also found articles that included hunting frames largely originated in Alberta, likely due to the threatened species listing and hunting moratorium. Attitudinal expressions included ecological, negative or neutral, and moral sentiments toward bears. The most common representative anecdote conveyed to the readership reflected the dire state faced by grizzly bears. The bear eco‐gossip expressed in the articles we reviewed, which included clear protagonists and antagonists and the occasional man‐bites‐bear surprise, appears to be the diet of manufactured information fed to the public. Results of our study can help scientists and conservationists understand how news media portrays grizzly bears to the public and how this might influence public sentiment toward their conservation, but also identifies the roles that scientists, conservationists and journalists together can play in crafting effective, factual and engaging news stories about bears.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".