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Record W2936337436 · doi:10.1038/s41436-019-0482-5

CRISPR in the North American popular press

2019· article· en· W2936337436 on OpenAlexafffundabout
Alessandro R Marcon, Zubin Master, Vardit Ravitsky, Timothy Caulfield

Bibliographic record

VenueGenetics in Medicine · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCRISPR and Genetic Engineering
Canadian institutionsInstitute of Health EconomicsUniversité de MontréalUniversity of Alberta
FundersGenome Canada
KeywordsCRISPRContext (archaeology)Social mediaPromotion (chess)Internet privacyPolitical scienceBiologyWorld Wide WebComputer scienceGeneticsLawPolitics

Abstract

fetched live from OpenAlex

PURPOSE: CRISPR is often called one of the century's most important discoveries and is commonly discussed in terms of its momentous potential impacts. This study analyzed how CRISPR is discussed in the North American popular press, including how it is defined, and which benefits and risks/concerns are attributed to the technology. METHODS: Using the Factiva database, we identified 228 relevant, nonduplicated articles containing either "CRISPR" or "C.R.I.S.P.R.," published in popular US and Canadian news sources between 1 January 2012 and 12 July 2017. Content analysis was performed on the articles. RESULTS: CRISPR is most often discussed in the context of human health (83.8%), compared with animals (26.3%) and plants (20.6%). Nearly all articles (96.1%) presented CRISPR's potential benefits; 61.4% of articles presented CRISPR-related risks/concerns, the vast majority of which focused on the uncertainty surrounding CRISPR, specifically with respect to germline modifications. CONCLUSIONS: Overall, the discourse suggests a strong promotion of CRISPR, but an element of caution is also evident. Technical as well as ethical, legal, and social risks/concerns play a prominent role. This media portrayal of CRISPR might help facilitate more sophisticated and balanced policy responses, where the scientific potential of the technology is highlighted alongside broader social considerations.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.153
Threshold uncertainty score0.391

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.010
GPT teacher head0.317
Teacher spread0.307 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations37
Published2019
Admission routes3
Has abstractyes

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