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Record W2270658095 · doi:10.4018/ijt.2016010105

Technoethical Inquiry into Ethical Hacking at a Canadian University

2016· article· en· W2270658095 on OpenAlexaffabout
Baha Abu-Shaqra, Rocci Luppicini

Bibliographic record

VenueInternational Journal of Technoethics · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicGovernment, Law, and Information Management
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsHackerSensemakingEngineering ethicsStakeholderPublic relationsSociologyKnowledge managementEthical decisionBusinessPolitical scienceComputer scienceEngineeringComputer security

Abstract

fetched live from OpenAlex

Business and academic organizations are in a constant pursuit of efficient and ethical technologies and practices to safeguard their information assets from the growing threat of hackers. Ethical hacking is one important information security risk management strategy they use. Most published books on ethical hacking have focused on its technical applications in risk assessment practices. This paper addressed a scarcity within the organizational communication literature on ethical hacking. Taking a qualitative exploratory case study approach, the authors explored ethical hacking implementation within a Canadian university as the case study in focus, applying technoethical inquiry theory paired with Karl Weick's sensemaking model as a theoretical framework. In-depth interviews with key stakeholder groups and a document review were conducted. Findings pointed to the need to expand the communicative and sociocultural considerations involved in decision making about ethical hacking organizational practices, and to security awareness training to leverage sensemaking opportunities and reduce equivocality.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.936
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.034
GPT teacher head0.335
Teacher spread0.301 · 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 designNot applicable
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

Citations8
Published2016
Admission routes2
Has abstractyes

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