Science News Agencies in SciComm: an exploratory index for evaluating and enhancing public interest in mass-distributed press releases
Bibliographic record
Abstract
Scientific press releases are reaching the public directly through press reproduction or institutional dissemination. Science News Agencies (SNAs) mediate this process, distributing texts to thousands of journalists while also "leaking" them on their websites and social media. This comparative case study examines four SNAs — BORI, SMC UK, AlphaGalileo, and EurekAlert! — regarding their role in circulating public scientific information. Through literature review and principles such as openness and inclusion in science, we converted scholars' concerns into a preliminary index potentially capable of assessing SNAs' public suitability. The SNAPI (Science News Agencies Public Index) suggests a shift from purely public relations content towards serving the public interest, highlighting areas needing attention in SNAs' social function, to be refined in future research. Clear guidelines, links to open scientific articles, and explicit notices on press releases’ purposes are simple yet effective ways to address issues concerning science public relations' pervasiveness in the public sphere.
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 | 0.019 | 0.094 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.024 | 0.037 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".