Bibliographic record
Abstract
This paper reports on the presentations from the second session of a 2-day workshop on genetic diversity and science communication, organized by the Institute of Genetics. The four talks in this session (by Sarah Cunningham-Burley, Gail Geller, Michael Hayden, and Theresa Marteau) focused on the topic of risk assessment in the context of genetic testing, screening and preventive medicine for complex disease. Each talk underscored the urgency and importance of evaluating when and for whom risk assessment may be useful. A recurrent theme was the need to attend closely to the diverse ways that risk is constructed, perceived and communicated in a variety of contexts and the significant implications of this for laypersons as well as experts. Although there was no consensus on when genetic risk assessment ceases (or might begin) to be useful, ensuing dialogue between presenters and participants reflected what is perhaps a new and critical engagement with how risk assessment itself is assessed. In response to this impetus, I use the word RISK as a heuristic to identify, extract and amplify four tendencies that appear to advance understandings of risk assessment towards a more explicitly reflexive, interpretive, and situated form of knowing.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".