Degrees of Separation: Mapping Publications Potentially Non-Compliant with STRAC
Bibliographic record
Abstract
Research security is of increasing concern to the Government of Canada, leading to a 2024 policy on sensitive technologies and affiliations of concern that restricts research collaborations by Canadian academics in sensitive technology research areas. This study examines whether Canadian researchers in sensitive technology fields have affiliations with Named Research Organizations (NROs) identified in the federal government’s Sensitive Technology Research and Affiliations of Concern (STRAC) policy. Over 500,000 articles published between January 2020 and June 2025 from the U15 group of Canadian universities and fifteen Government of Canada departments were analyzed for collaborations with named research organizations. Of these articles, 12,320 academic and 401 government publications involved a coauthorship with at least one individual with an NRO affiliation. A far smaller number — 1,096 academic and 12 government articles — involved an NRO collaboration in a sensitive technology research project. There is a distinct decline in sensitive technology NRO collaborations following STRAC’s implementation, suggesting some deterrent effect. The three universities with the highest rates of NRO-sensitive technology collaborations (Alberta, Toronto and Waterloo) collaborate most frequently with Chinese NROs collaborate, commonly in electrical engineering and computer science fields. This project provides a transparent methodology to the Canadian sensitive technology research community that can help in assessing security risks surrounding research partnerships.
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 | 0.035 | 0.284 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.056 | 0.097 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".