How Do Online News Genres Take Up Knowledge Claims From a Scientific Research Article on Climate Change?
Bibliographic record
Abstract
The Internet has helped to change who writes about science in the news, how news is written, and how it is taken up by different audiences. However, few studies have examined how these changes have impacted the uptake of scientific claims in online news writing. This case study explores how online news genres take up knowledge claims from a research article on climate change over a period of one year and shows how shifting boundaries between rhetorical communities affect genre uptake. The study results show that online news writers predominantly use the news report genre to cover research findings for 48 hours, after which they predominantly use the news editorial genre to engage these findings. Analysis suggests that the news report genre uses the press release and the article abstract as intermediary genres, but the news editorial uses only the abstract. I argue that the switch between genres repositions the scientist, the journalist, and the public epistemologically, a reorientation that favors uptake in news media outlets supporting action to mitigate climate change and its effects.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.084 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".