Cybersecurity in North America: a review of Mexico
Bibliographic record
Abstract
The chapter examines Mexico's shifting cybersecurity landscape, highlighting its initiatives, obstacles, and cooperation within North America. Post-September 11, 2001, security gained precedence over trade, significantly impacting regional integration efforts. The growing digital connectivity among Mexico, Canada, and the US heightens susceptibility to cyber threats, demanding robust security measures. It showcases Mexico's strides in crafting national cybersecurity strategies, fostering public-private partnerships, and engaging in international collaboration frameworks. Despite progress, Mexico contends with substantial hurdles, including cyber assaults from cartels, legislative gaps, and the geopolitical complexities of foreign technologies, particularly Huawei's role in its telecommunications sector. Proposed measures like the Federal Cybersecurity Law and the National Digital Strategy 2021–2024 aim to fortify Mexico's cyber defenses. However, the potential dissolution of vital institutions like the Federal Institute for Access to Public Information and Data Protection (INAI) poses data security and transparency risks. The chapter stresses collaborative cybersecurity efforts across North America for resilient digital ecosystems. It also explores the impact of technologies like 5G and cloud computing, underscoring the need for comprehensive regulatory frameworks to tackle emerging threats. The chapter advocates for sustained investment in cybersecurity infrastructure and workforce development to bridge the commitment-capability gap.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".