Bridging the Digital Divide and Mitigating Cyber Security Risks in Canada
Bibliographic record
Abstract
Canada's rapid digital transformation has created significant opportunities but also intensified existing inequalities and cyber security vulnerabilities. To better understand these challenges, an analysis is conducted at both individual and firm levels using recent Statistics Canada data and advanced econometric methods. At the individual level, data from the 2020 Canadian Internet Use Survey reveal how socioeconomic and demographic characteristics such as age, education, income, gender, and Indigenous identity influence digital engagement. A survey-weighted debiased Lasso logit model captures complex interactions among these factors, while cluster analysis assesses how provincial pandemic measures affected internet use and digital adoption during COVID-19. Firm-level analysis incorporates data from the 2021 Canadian Survey of Digital Technology and Internet Use and the 2021 Canadian Survey of Cyber Security and Cybercrime. A Business Digital Usage Score quantifies firms' adoption of advanced technologies such as cloud computing, data analytics tools, and artificial intelligence. Stochastic frontier analysis evaluates how close firms are to their technological frontier. A survey-weighted debiased Lasso logit model identifies factors associated with both digital adoption and cyber security vulnerabilities across industries and firm sizes. The Independence of Irrelevant Alternatives assumption in Multinomial Logit models is critically evaluated through simulation experiments examining the performance of the Hausman-McFadden (HM) specification test. The HM test is assessed under a number of data-generating scenarios used to mirror real-world applied research scenarios. To address challenges posed by high-dimensional data, the study introduces a Hausman test based on a debiased Lasso estimator.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".