Bottoms Up: A Comparison of Voluntary Cybersecurity Frameworks
Bibliographic record
Abstract
"Although there is a spectrum of cybersecurity regulatory frameworks emerging around the world ranging from more state-centric approaches to voluntary initiatives, more and more nations — including the United States — seem to be settling on a bottom-up approach to enhancing private-sector cybersecurity. Emblematic of this movement in the U.S. context is the 2014 National Institute for Standards and Technology (NIST) Cybersecurity Framework. This Framework, which is comprised partly of regularly updated cybersecurity best practices, has already been influential in shaping the field of cybersecurity due diligence not only in the United States, but also in nations ranging from Canada to India. However, there has not yet been a thorough examination of the similarities and differences between these various bottom-up approaches and the extent to which they are promoting the harmonization of cybersecurity best practices. This Article addresses this omission by investigating a subset of national approaches to cybersecurity policymaking highlighting the extent to which they are converging and diverging using the NIST Framework as a baseline for comparison. Such an understanding is vital not only to businesses operating across these jurisdictions, but also to policymakers seeking to leverage the expertise of the private sector in promoting cyber peace."
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 | 0.051 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.013 | 0.019 |
| Scholarly communication | 0.026 | 0.016 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".