Environmental DNA as Novel Technology: Lessons in Agenda Setting and Framing in News Media
Bibliographic record
Abstract
Environmental DNA (eDNA) is an emerging technology used for understanding ecosystems, environmental change, and stressors. Cellular and extracellular DNA are collected from environmental samples instead of individual wildlife animals, and as such eDNA comes with associated logistical and ethical benefits. It is increasingly being used, yet to date public knowledge and perceptions of eDNA have not been explored. Given that most of the public gathers scientific information from news media sources, this is a logical first place to start. This paper reports on a framing and agenda-setting analysis of news media coverage of eDNA in Canada and the United States from 2000 to 2020. The findings indicate that eDNA is being framed as an emerging and powerful tool, although questions regarding its validity and reliability are raised vis-à-vis identifying the presence of invasive species. Less than half of the news articles analyzed address broader social or ethical issues in relation to eDNA, and the majority focus on the potential financial impacts of eDNA findings on development projects and business interests. The potential ethical advantages of non-lethal sampling methods used via eDNA sampling are not addressed, nor are the potential ethical issues raised by its potential use in bioprospecting, indicating that the current state of agenda setting regarding eDNA in these newspapers is focused on economic impacts, to the exclusion of potential ethical issues. This unfolding news coverage will likely be key to understanding public perceptions of this novel technology.
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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 | 0.080 | 0.122 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.013 | 0.041 |
| Scholarly communication | 0.046 | 0.036 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".