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Record W2587898185 · doi:10.1080/1369118x.2017.1285951

‘Warren Buffet is my cousin’: shaping public understanding of big data biotechnology, direct-to-consumer genomics, and 23andMe on Twitter

2017· article· en· W2587898185 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInformation Communication & Society · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicMedia Studies and Communication
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsFraming (construction)Big dataGenomicsCousinPublic discourseGenomic medicinePolitical sciencePublic relationsSociologyBiotechnologyData scienceGeneticsBiologyGenomeEngineeringComputational biologyComputer scienceLawGene

Abstract

fetched live from OpenAlex

Scholars, educators, regulators, pundits, and other observers are advocating for regulation and oversight of direct-to-consumer (DTC) genomic testing. As a result, the technology has been subject of highly visible public and regulatory controversy. In this article, we explore the nature and the shape of the sentiment of public discourse about the DTC company, 23andMe. We conduct a quantitative content analysis and qualitative framing analysis on Tweets. We find that the discourse surrounding DTC genomics and 23andMe is largely positive. We also identify a number of frames users deploy to debate, discuss, and share their experiences with DTC genomics and 23andMe. We argue that these frames create meaning around this emerging technology for its users.

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.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.827
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.001
Scholarly communication0.0000.001
Open science0.0020.002
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 head0.317
GPT teacher head0.364
Teacher spread0.047 · 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