A Survey of Ethical Agreements in Information Security Courses
Bibliographic record
Abstract
Existing ethical agreements, as applicable in the teaching of information security courses, typically spell out rules on what students should and should not do. The main problem is that the question of what students should or should not do is not a settled issue, because personal stances on questions of morality and ethics fundamentally influence the ethical recommendations that teachers present to their students. In light of the growing level of malice in the computing domain, experts have highlighted the importance of information security ethics by debating the need for a standard code of ethics for information security. Arguably, differences in ethical stance, with the effect of divergent ethical agreements, will not efficiently serve the purpose of effective universal application of ethics in the field of information security education. Examining current ethical policies in information security courses can provide insight about the prevailing ethics within the information security community. Moreover, understanding what the prevailing philosophies on ethics are within the community, in terms of how they actually diverge or converge, will present a good projection of how a standard policy on ethics may be feasibly applicable in a future regulatory environment. This way, we may be able to forecast the nature of ethical norms that future professionals will accept or allow to be imposed on them. Therefore, in our survey, we analyze ethical agreements on information security courses to identify the nature of existing agreements. We determine the commonalities of these agreements and derive an ethical policy prototype that includes the common elements of 329 ethical policies.
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 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 | 0.019 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".