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Record W2939001363 · doi:10.1080/08874417.2019.1571458

Knowledge Management for Cybersecurity in Business Organizations: A Case Study

2019· article· en· W2939001363 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

VenueJournal of Computer Information Systems · 2019
Typearticle
Languageen
FieldComputer Science
TopicInformation and Cyber Security
Canadian institutionsSaint Mary's University
Fundersnot available
KeywordsComputer securityDomain (mathematical analysis)Process (computing)Knowledge managementBusiness processComputer scienceBusiness intelligenceBusinessProcess managementWork in process

Abstract

fetched live from OpenAlex

Knowledge management (KM) plays important roles in cybersecurity. This study collects five real-life cases of good practices of KM for the domain of cybersecurity in business organizations. Through an iterative process of team-based qualitative data analysis of the five cases, the study develops a model of KM for cybersecurity that conceptualizes three common aspects of KM practices for cybersecurity in business organizations. First, KM for cybersecurity in business organizations has clear specialized organizational structures that involve three inter-organizational tiers across the organization boundaries. Second, the knowledge flows of KM for cybersecurity in business organizations emphasize on explicit, declarative, and specific knowledge. Third, in comparison with KM for other domains, KM for cybersecurity has well-defined objective measures to assess the effectiveness of KM. The domain-specific KM model based on the good practice cases provides a road-map for KM practices in the domain of cybersecurity in business organizations.

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)0.001
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.840
Threshold uncertainty score0.519

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.005
Open science0.0010.000
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 head0.012
GPT teacher head0.254
Teacher spread0.242 · 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