Assessing dual use research of concern (DURC)—lessons learned from the United States government institutional DURC policy
Bibliographic record
Abstract
Life science research was analyzed for potential misuse in the 2004 report "Biotechnology Research in an Age of Bioterrorism". However, it was not until 2015 that the United States Government (USG) Institutional Dual Use Research of Concern (DURC) policy went into effect. Institutions receiving USG funding for life science research are required to scan their research portfolios for research involving one of 15 agents and subsequent 7 experimental effects described in the policy. In practice, this policy was implemented in a variety of ways with varying outcomes and lessons learned. First and foremost, reviewing research for potential DURC is a highly subjective process that differs depending on the risk tolerance, experience, and training of the individuals charged with reviewing research for an institution as well as the review process itself. The information being reviewed also lends to the subjectivity of the process, that is, the experimental data provided. It is difficult to determine whether research is potential DURC without experimental data. Any review process is hypothetical until there is data. Lastly, reviewers of the research should look beyond the research proposals, like how compounding existing research information can create new risks, potential use in other organisms or systems, or the creation of a roadmap that, for example, shows how to create a concerning organism or could be used in a pathogen.
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 | 0.753 | 0.774 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.018 | 0.011 |
| Science and technology studies | 0.013 | 0.030 |
| Scholarly communication | 0.053 | 0.034 |
| Open science | 0.015 | 0.029 |
| Research integrity | 0.033 | 0.030 |
| Insufficient payload (model declined to judge) | 0.004 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".