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Record W4321221162 · doi:10.1515/9781399512510

Cyberspace and Instability

2023· book· en· W4321221162 on OpenAlexfundno aff

Bibliographic record

VenueEdinburgh University Press eBooks · 2023
Typebook
Languageen
FieldSocial Sciences
TopicCybersecurity and Cyber Warfare Studies
Canadian institutionsnot available
FundersU.S. Naval War CollegeInstitute for National Strategic StudiesCenter for International Security and Cooperation, Stanford UniversityNational Institute of Standards and TechnologyUniversity of TorontoEidgenössische Technische Hochschule ZürichU.S. Department of DefenseEuropean CommissionGeorgetown UniversityEli Lilly and CompanyHarvard UniversityU.S. Department of StateNational Security AgencyUniversity of OxfordNational Science Foundation
KeywordsCyberspaceInstabilityComputer sciencePhysicsThe InternetWorld Wide WebMechanics

Abstract

fetched live from OpenAlex

A wide range of actors have publicly identified cyber stability as a key policy goal but the meaning of stability in the context of cyber policy remains vague and contested: vague because most policymakers and experts do not define cyber stability when they use the concept; contested because they propose measures that rely – often implicitly – on divergent understandings of cyber stability. This is a thorough investigation of instability within cyberspace and of cyberspace itself. Its purpose is to reconceptualise stability and instability for cyberspace, highlight their various dimensions and thereby identify relevant policy measures. It critically examines both ‘classic’ notions associated with stability – for example, whether cyber operations can lead to unwanted escalation – as well as topics that have so far not been addressed in the existing cyber literature, such as the application of a decolonial lens to investigate Euro-American conceptualisations of stability in cyberspace.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.009
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.016
Scholarly communication0.0070.008
Open science0.0000.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0090.001

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.

Opus teacher head0.030
GPT teacher head0.253
Teacher spread0.224 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreOther

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".

Quick stats

Citations2
Published2023
Admission routes1
Has abstractyes

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