Bibliographic record
Abstract
At-home DNA testing remains popular amongst individuals today.These direct-toconsumer services come with several privacy risks, that can extend far beyond the individuals taking the test.How do participants attribute risk to biological family members?How do users and non-users differ in comfort with their data being shared, and their understanding of privacy risks?How do privacy perceptions differ for ancestry and health data?To investigate these questions, we conducted a 2 × 2 survey, and discovered non-users were significantly more privacy conscious, and that health data was considered more beneficial overall.We then interviewed 10 biological family members of users who had not taken a test themselves; though many were unconcerned or indifferent towards privacy, privacy-conscious participants were frustrated by, and resigned to, the loss of control over their data.We discuss our findings, the implications of our research, offer recommendations to improve privacy, and identify areas for future research.This thesis is the result of support and guidance from many over the years.To my supervisor, Sonia Chiasson: I would never have found my love for research and HCI had I not stumbled across CHORUS as an undergraduate in 2015.You have my deepest gratitude, for the infinite patience, feedback, and guidance.You are a stellar mentor, and have always been there for your students (rain or shine).I feel privileged to have been mentored by you,
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 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 | 0.012 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".