Governing 'dual-use' research in Canada: A policy review
Bibliographic record
Abstract
National and international organisations have implemented governance mechanisms to address a diversity of ethical, security and policy challenges raised by advances in research and innovation. These challenges become particularly complex when research or innovations are considered ‘dual-use’, i.e. can lead to both beneficial and harmful uses, and in particular, civilian (peaceful) and military (hostile) applications. While many countries have mechanisms (i.e. export controls) to govern the transfer of dual-use technology (e.g. nuclear, cryptography), it is much less clear how dual-use research from across the range of academic disciplines can or should be governed. Using the Canadian research and policy context as case study, this paper will first, examine the governance mechanisms currently in place to mitigate the negative implications of dual-use research and innovation; second, compare these with other relevant international governance contexts; and finally, propose some ways forward (i.e. a risk analysis approach) for developing more robust governance mechanisms.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Methods · Genre: Review About the Canadian research system: yes · About a Canadian topic: yes | Not applicable | low |
| gpt | MetaresearchScience and technology studies Domain: Methods · Genre: Review About the Canadian research system: yes · About a Canadian topic: yes | Other design | high |
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 | 0.026 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.016 | 0.031 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".