MétaCan
Menu
Back to cohort
Record W4281854112 · doi:10.1007/s10611-022-10037-y

Exploring the social implications of buying and selling cyber security

2022· article· en· W4281854112 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCrime Law and Social Change · 2022
Typearticle
Languageen
FieldComputer Science
TopicCybercrime and Law Enforcement Studies
Canadian institutionsUniversity of Waterloo
FundersDeakin University
KeywordsCommodificationBusinessRelevance (law)Organised crimeCritical security studiesSovereigntyEconomicsCloud computing securityNetwork security policySociologyPolitical scienceCloud computingEconomyPoliticsLawCriminology

Abstract

fetched live from OpenAlex

Abstract Governments, businesses, private citizens and even organised crime are increasingly investing in cyber security, with the cyber security industry growing in size and relevance. This paper demonstrates that markets for the buying and selling of cyber security should be subject to many of the same critical inquiries typically targeted at the private security industry. Using a number of illustrative examples of emerging trends in the commodification of cyber security it will be highlighted how these markets create significant social impacts and present similar dilemmas of democracy, justice, sovereignty, and deleterious side-effects for wider society. Key conceptual differences between cyber security commodities and ‘conventional’ security commodities will also be considered before arguing for an inter-disciplinary research agenda into the considerable social implications of the buying and selling of cyber security commodities.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.723
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.204
GPT teacher head0.310
Teacher spread0.106 · 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