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Record W3105366519 · doi:10.1093/eep/dvaa015

Communicating science: epigenetics in the spotlight

2020· review· en· W3105366519 on OpenAlexafffund
Stephanie O. M. Dyke, Catherine Ennis, Yann Joly, Jörn Walter, Reiner Siebert, Tomi Pastinen

Bibliographic record

VenueCurrent Zoology · 2020
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRace, Genetics, and Society
Canadian institutionsMcGill University and Génome Québec Innovation CentreLearning PartnershipUniversity of British ColumbiaMcGill UniversityMontreal Neurological Institute and Hospital
FundersNational Institute of Environmental Health SciencesCanadian Institutes of Health ResearchNational Institutes of HealthBC Children's HospitalBundesministerium für Bildung und ForschungGenome Institute of SingaporeMcGill University
KeywordsEpigeneticsBiologyEvolutionary biologyGeneticsGene

Abstract

fetched live from OpenAlex

Given the public interest in epigenetic science, this study aimed to better understand media representations of epigenetics in national newspaper coverage in various regions in North America, Europe, and Asia. Content analysis was used to study media messages about epigenetics, their policy focus, and the balance of the reporting. We identified several recurring themes in the news reports, including policy messages relating to individual and societal responsibilities. We also found shortcomings in the media's portrayal of epigenetic science, and sought to identify potential causes by considering the underlying scientific evidence that the media reported on. A case study analysis showed that the results of epigenetic studies were often overstated in academic research publications due to common experimental limitations. We suggest that defining standardized criteria with which to evaluate epigenetic studies could help to overcome some of the challenges inherent in translating complex epigenetic research findings for non-technical audiences, and present a Press Kit template that researchers can adapt and use to aid in the development of accurate and balanced press releases.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.997
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0020.001
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 head0.061
GPT teacher head0.380
Teacher spread0.319 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations15
Published2020
Admission routes2
Has abstractyes

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