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Record W3047105104 · doi:10.24989/ocg.338.13

Cyber Security Master's degrees in the United Kingdom: a comparative analysis

2020· article· en· W3047105104 on OpenAlexfundno aff
Anna Urbanovics, Péter Sasvári

Bibliographic record

VenueCentral and Eastern European eDem and eGov Days · 2020
Typearticle
Languageen
FieldComputer Science
TopicInformation and Cyber Security
Canadian institutionsnot available
FundersRoyal Holloway, University of LondonUniversity of SurreyQueen's UniversityCranfield UniversityNewcastle UniversityUniversity of OxfordQueen's University BelfastUniversity of Southampton
KeywordsInformation securityCertified Information Systems Security ProfessionalThematic analysisHigher educationPolitical scienceNational securityKingdomComputer securityPublic relationsSecurity serviceComputer scienceSociologyNetwork security policyQualitative research

Abstract

fetched live from OpenAlex

In today’s digitized world, where most of our activities are related to online platforms, the information security has become more essential than ever. Most countries have launched national strategies for the implementation of cyber security. In these, the education and training of information security professionals get particular roles. The National Cyber Security Centre created a common framework for cyber security education in the United Kingdom for the universities offering degrees in information security. The aim of this paper is to examine and compare the British cyber security degrees. The first chapter examines and compares the British universities and degrees from a theoretical aspect, including the necessity of these programs. The second chapter examines the degrees from several aspects based on the data of the Scopus database, with a special focus on the thematic modules, and the academic activities of the 1,650 examined university instructors.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.519
Threshold uncertainty score0.391

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.068
GPT teacher head0.248
Teacher spread0.180 · 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 designObservational
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
Published2020
Admission routes1
Has abstractyes

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