Science of Security: Combining Theory and Measurement to Reflect the Observable
Bibliographic record
Abstract
What would a “Science of Security” look like? This question has received considerable attention over the past 10 years. No one argues against the desirability of making security research more “scientific.” But how would one would go about that? We argue that making progress on this requires clarifying what “scientific” means in the context of computer security, and that has received too little attention. We pursue this based on a review of literature in the history and Philosophy of Science and a belief that work under the theme “Science of Security” should align with and ideally, benefit from what has been learned over a few hundred years in science. We offer observations and insights, with a view that the security community can benefit from better leveraging past lessons and common practices well-accepted by consensus in the mainstream scientific community—but which appear little recognized in the security community.
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 | 0.033 | 0.095 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.004 | 0.050 |
| Scholarly communication | 0.017 | 0.049 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.007 | 0.012 |
| 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".