Witnessing the Genetic Self: How Non-Specialists use Reveal Videos to Approach Genetics and Race Through Direct-To-Consumer Genetic Ancestry Testing
Bibliographic record
Abstract
This paper investigates the interpretation and expression at work when those without a higher education in genetics take a direct-to-consumer (DTC) genetic ancestry test (i.e. AncestryDNA) and then communicate this experience through online video on YouTube, most commonly through the Reveal genre of videos. Through non-random quota sampling a diverse corpus for analysis was created and then analyzed through the lenses of critical race theory, intersectionality, and María Lugones’s concepts of transparency and thickness, with focusing guidance from Gubium and Holstein’s narrative components to uncover how the test-takers approached genetics and race. The variations in how individuals approach their DTC genetic ancestry test results and communicate them through the videos, touching on topics such as race, family, self-identity, and stories, were discovered to work well alongside Roth and Ivemark’s recently presented genetic options theory.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".