News Media Representations of Gene Transcriptional Profiling: How Do the Culture/Nature Binary and Animal Ethics Factor In?
Bibliographic record
Abstract
Abstract Developments in genomics, notably the development of transcriptomics, have enabled the “molecularization of life” (Rose, 2001, p.13). Most analyses of these developments focus on human medicine. Other realms warrant attention, specifically applications with nonhuman animals, which are increasingly likely in response to growing concerns regarding their adaptability to rapid environmental changes. Given that public understandings can impact adoption of novel technologies in important ways, and that the public mostly receives information about technologies from news media, this article examines communication of knowledge and socioethical (particularly animal ethics) considerations regarding transcriptomics in news media. One key consideration examined is the potential collapsing of the culture/nature binary in light of transcriptomics developments forecasted in the literature. We find that explicit attention to socioethical implications is least likely when nonhuman animals are involved, whereas it most often occurs in relation to ways transcriptomics could be used to unsettle what constitutes human nature, illustrating the ongoing power of the culture/nature binary.
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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 | 0.011 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".