Proceedings of the Dual Use Research of Concern Panel Discussion: challenges and perspectives
Bibliographic record
Abstract
To address real and perceived emerging risks originating from the ever-accelerating breakthroughs in life science research, the Dual Use Research of Concern (DURC) Panel Discussion, organized by Synbio Canada and the Alberta RNA Research and Training Institute (ARRTI), took place on June 23rd, 2021. It brought together six stakeholders from different levels of academic research, administration, governance, and science publishing to explore the current and future challenges in addressing DURC. Technological advancements within the life sciences, especially within the field of omics technology, make it difficult to apply a simple checklist for dual-use assessment and require continuous and integrated effort. Bottom-up approaches from within the scientific community are suggested by all stakeholders to enable efficient governance and address the true risks resulting from DURC, not just the alleged risks. To address such alleged risks, open and broadscale communication of DURC and its oversight policies may be required. At the same time, any form of open communication also contains the risk of information hazards, defined as potentially creating public fear or informing malicious actors. Here, an overview of the DURC panel and its outcomes is provided.
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 | 0.149 | 0.149 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.025 | 0.008 |
| Scholarly communication | 0.027 | 0.010 |
| Open science | 0.005 | 0.018 |
| Research integrity | 0.040 | 0.038 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".