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Record W3047331065 · doi:10.1080/14636778.2020.1799343

The consumer representation of DNA ancestry testing on YouTube

2020· article· en· W3047331065 on OpenAlexafffund
Alessandro R Marcon, Christen Rachul, Timothy Caulfield

Bibliographic record

VenueNew Genetics and Society · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCRISPR and Genetic Engineering
Canadian institutionsUniversity of ManitobaUniversity of Alberta
FundersCanadian Institutes of Health ResearchGenome AlbertaGenome Canada
KeywordsGenetic genealogyEthnic groupRace (biology)Representation (politics)Promotion (chess)Identity (music)Social mediaPhrasePsychologySociologyAnthropologyGender studiesComputer scienceWorld Wide WebPolitical scienceDemographyLawPoliticsAesthetics

Abstract

fetched live from OpenAlex

The growth of consumer DNA ancestry testing has resulted in questions and critiques being raised in social and research contexts. This study examined individuals discussing their ancestry DNA testing results on YouTube by searching for the two most popular testing companies (23andMe; Ancestry) and the phrase “DNA results.” The finalized dataset consisted of 117 videos, on which directed content analysis was performed. In the videos, individuals used results to clarify, confirm, question, and re-evaluate their previously held conceptions of racial/ethnic identities. Reactions were more positive than negative (88.1% vs. 8.1%), and results more commonly reaffirmed (77.8%) than re-conceptualized (40.0%) one’s racial/ethnic identity. Ancestry testing and personal social media accounts were commonly promoted, demonstrating biotechnological hype where promotion abounds and critiques are scarce. Questions persist around the impact of ancestry DNA testing in reifying a scientifically inaccurate conception of race and what impact YouTube videos might have on audiences.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score0.231

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 head0.038
GPT teacher head0.321
Teacher spread0.283 · 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 designBench or experimental
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

Citations16
Published2020
Admission routes2
Has abstractyes

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