Communicating with the Public about Emerald Ash Borer: Militaristic and Fatalistic Framings in the News Media
Bibliographic record
Abstract
Invasive species can spread to new landscapes through various anthropogenic factors and negatively impact urban ecosystems, societies, and economies. Public awareness is considered central to mitigating the spread of invasive species. News media contributes to awareness although it is unclear what messages are being communicated. We incorporated Frame Theory to investigate newspapers’ coverage of the emerald ash borer (EAB; Agrilus planipennis Fairmaire (Coleoptera: Buprestidae)), which has killed millions of ash trees in the continental United States. We conducted a content analysis of 924 news articles published between 2002 and 2017 to examine language framing (how a phenomenon like invasive species is constructed and communicated), information sources, management methods, recommended actions for the public and whether this communication changed overtime. Seventy-seven percent of articles used language evocative of distinctive risk framings, with the majority of these using negative attribute frames like invasion-militaristic and/or fatalistic language to describe EAB management. Few discussed positive impacts like galvanizing public support. Most articles used expert sources, primarily government agents. We recommend that public communications regarding invasive species be cautious about language evoking militarism and fatalism. Furthermore, invasive species communication requires a broader diversity and representation of voices because invasive species management requires community effort.
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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 | 0.013 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".