Bibliographic record
Abstract
The Canadian Government recently introduced a new document entitled “Strong, Secure, and Engaged” (SSE) outlining Canada’s Defense Policy across a wide-range of its activities. One very new factor of SSE is the decision to develop active cyber-attack capabilities to potentially employ against potential adversaries. This raises some key issues including: (i) the potential implications of using cyber-attacks, (ii) the potential for unintended consequences arising as a result of using such, and (iii) the risks associated with subsequent use against the attacker either intentionally or accidentally. Overarching questions include defining under what circumstances cyber-attacks should be permitted and what should be done to ensure they cannot subsequently be used against us or lead to harming one of our allies? Canada’s allies have already developed and deployed such weapons with some demonstrable success but with some unintended consequences. What can we learn from the available information about the safe use of cyber-attacks and when is it reasonable to use such a weapon? The nature of this technology is different than other forms of military aggression used in either peace or war time. What checks and balances need to be put in place to ensure that it is used only under appropriate government-authorized military oversight? What protections can be put in place to ensure that the inadvertent release of a cyber-attack cannot occur? Finally, the decision to endorse the development a cyber-attack capability introduces a difficult dichotomy. Cyber-attack technology exploits discovered weaknesses in digital systems. In a regime that only permits cyber-defence activities, the discovery of weaknesses and deploying a repair for the discovered weakness is an obvious choice. However, if Canada is to incorporate a cyber-attack strategy, the decision to repair the weakness must now be traded-off against exploiting the weakness against an enemy. These undiscovered weaknesses are known as zero-day attacks because a previously unknown vulnerability in a computer system (hardware or software) is exploited “on the same day” the vulnerability becomes known to the wider world.
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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.015 | 0.037 |
| Scholarly communication | 0.030 | 0.037 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.009 | 0.018 |
| Insufficient payload (model declined to judge) | 0.022 | 0.009 |
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".