Bibliographic record
Abstract
The information age has opened a new front of adversarial statecraft. The past decades have seen the rise and refinement of conflict enacted in the world of information, with tactics including seeding disinformation, the theft of sensitive data, confusing or obscuring public opinion to forward specific goals, and beyond. Deterrence in the 21st Century asks how, and if it is indeed possible, to deter an enemy in the realm of information warfare. \n \nSetting the stage with an overview of key concepts of deterrence in the information age, the book presents new conceptual approaches and their possible applications. Bringing together some of the most respected analysts working today, Deterrence in the 21st Century looks beyond the technical aspects of the use of information and disinformation as adversarial statecraft to seek new avenues to deter the undermining of institutions and societies. \n \nTreating deterrence as a concept, a policy, a social challenge, and a series of practical solutions, Deterrence in the 21st Century presents theoretical approaches, conceptual analysis, empirical research, and content analysis. This is a thorough, thoughtful, and expert analysis of one of the most difficult and essential security challenges of our time. \n \nWith contributions by: Christopher Ankersen, Yair Ansbacher, Oshri Bar-Gill, Stephen J. Cimbala, Maddie D’Agata, Molly Ellenberg, Leandre R. Fabrigar, Rachel Lea Heide, Nicole J. Jackson, Pierre Jolicoeur, Christian Leuprecht, Adam Lowther, Sarah Jane Meharg, Eric Ouellet, Ronald D. Porter, Anthony Seaboyer, Ron Schleifer, Miniqian Shen, Anne Speckhard, Keith Stewart, Joseph Szeman, and Alex Wilner
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.012 | 0.009 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.033 | 0.008 |
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; both teacher heads agree on what is shown here.
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".