A Technoethical Study of Ethical Hacking Communication and Management Within a Canadian University
Bibliographic record
Abstract
Ethical hacking is an important information security risk management strategy within higher education applied against the growing threat of hacking attacks. Confusion regarding the meaning and ethics of ethical hacking within broader society and which resonates within organizations undermines information security. Confusion within organizations increases unpredictably (equivocality) in the information environment, which raises risk level. Taking a qualitative exploratory case study approach, this chapter pairs technoethical inquiry theory with Karl Weick's sensemaking model to explore the meanings, ethics, uses and practices, and value of ethical hacking in a Canadian university and applies technoethical inquiry decision-making grid (TEI-DMG) as an ethical decision-making model. Findings point 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 in the information environment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.050 | 0.017 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".