GOVERNMENT-MEDIA RELATIONS IN CANADA’S FEDERAL CYBER SECURITY (JULY - DECEMBER 2018 & 2021)
Bibliographic record
Abstract
The world is increasingly technologically connected, and those technologies are advancing at rapid pace, meaning that cyber security can seem like whack-a-mole. Malicious cyber activity plays heavily in geopolitics and governments worldwide are faced with challenges to protect their citizens and sovereignty. This research evaluates the Government of Canada’s approach to communicating with the public about cyber security activities and programming based on representations in traditional news media. It develops a model of government-media relations in federal cyber security activities and programming based on cross-sectional analysis of two six-month periods (from July – December in 2018 and 2021). These periods correspond to the six months following the release of Canada’s most recent highest-level strategy for cyber security, the National Cyber Security Strategy, and a contemporary period. It assesses the representations and analyzes some relevant themes throughout like the Huawei 5G story that spans the timeframe. This is largely an assessment of what gets picked up in traditional news media and contextually why that is. There appeared to be a predominantly information dissemination model of government-media relations which neither favoured or dissented the Government of Canada’s activities and programming in cyber security. There was some evidence of a two-way asymmetric model, but this could not be determined within the scope of this analysis in evaluating traditional news media content.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".