MétaCan
Menu
Back to cohort
Record W4285529344 · doi:10.47833/2021.3.csc.005

Domino effect and other models in the IT process

2021· article· en· W4285529344 on OpenAlexaff
Krisztina Győrffyné Holló, Adam Kariszt

Bibliographic record

VenueGradus · 2021
Typearticle
Languageen
FieldComputer Science
TopicInformation and Cyber Security
Canadian institutionsMagna International (Canada)
Fundersnot available
KeywordsRisk analysis (engineering)Domino effectIdentification (biology)Information security managementComputer scienceProcess (computing)DominoRisk managementInformation securityPersonally identifiable informationSecurity managementComputer securityKnowledge managementData scienceSecurity information and event managementBusinessCloud computing security

Abstract

fetched live from OpenAlex

Based on the information security principles, it is essential to manage the risk of the information systems that collect, store and manage personal data. It can be existed many models and methods for risk identification, analyzing and evaluation, which ensues from each other and often evolving together with information technology. The present research aims to answer whether accident and risk management methods are suitable for identifying, analyzing, and mitigating information security risks.

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 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 categoriesnone
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 score0.660
Threshold uncertainty score0.110

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.018
GPT teacher head0.257
Teacher spread0.239 · 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 teacher head, not a consensus.

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

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

Citations0
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueGradusSame topicInformation and Cyber SecurityFrench-language works237,207