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Record W4402414422 · doi:10.1590/scielopreprints.9799

Science News Agencies in SciComm: an exploratory index for evaluating and enhancing public interest in mass-distributed press releases

2024· preprint· en· W4402414422 on OpenAlexaff
Monique Batista de Oliveira, Mariana Hafiz, Alice Fleerackers, Luiá Bolonha Nunes, Germana Barata

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicRadio, Podcasts, and Digital Media
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsIndex (typography)Exploratory researchPolitical scienceBusinessComputer scienceWorld Wide WebSociologySocial science

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.409
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.279
GPT teacher head0.413
Teacher spread0.135 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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