Bibliographic record
Abstract

 
 
 Four key events are addressed in this briefing note. Key event one is the announcement in April and May of 2017 with the launch of two supercomputers in Canada (Graham at University of Waterloo; Cedar at Simon Fraser University) and a third (Niagara at The University of Toronto) using Compute Canada’s Resources Allocation (Compute Canada, 2018a). Key event two is the announcement that Huawei Canada is building Graham’s operating system (Feldman, 2017). Key event three entails CSIS being warned by the US Senators (Rep. Sen Marco Rubio and Dem. Sen Mark Warner) about the possibility of China and Russia spying on Canada. Key event four, the United States has reportedly banned sales of Huawei products on US military bases (Bronskill, 2018; Collins, 2018).
 This briefing note is particularly relevant as Compute Canada is now preparing for 2019 resource allocation; there may be a raised/elevated security risk of economic espionage intellectual property theft and abusing education access privileges which need to be considered (SFU Innovates Staff, 2018).
 
 
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →2 of 3 models called this metaresearch. This work is contested: it sits on the field's empirical boundary, and whether it counts depends on which model you asked. It is one of the 51 works in the disagreement dossier.
Briefing note assessing espionage and security threats to Compute Canada's national supercomputing infrastructure; object is the governance of Canadian research infrastructure, though it sits on the T2/T3 boundary as a policy briefing.
The briefing analyzes security risks and responses involving Canadian research supercomputing infrastructure.
Policy briefing on security threats to Canadian research supercomputers, relevant to the research ecosystem but not analytic metaresearch.
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.019 | 0.002 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.055 | 0.007 |
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".