Bibliographic record
Abstract
Abstract In “A Brief History of Information Ethics,” Thomas Froehlich (2004) quickly surveyed under several broad categories some of the many issues that constitute information ethics: under the category of librarianship—censorship, privacy, access, balance in collections, copyright, fair use, and codes of ethics; under information science, which Froehlich sees as closely related to librarianship—confidentiality, bias, and quality of information; under computer ethics—intellectual property, privacy, fair representation, nonmaleficence, computer crime, software reliability, artificial intelligence, and e‐commerce; under cyberethics (issues related to the Internet, or “cyberspace”)—expert systems, artificial intelligence (again), and robotics; under media ethics—news, impartiality, journalistic ethics, deceit, lies, sexuality, censorship (again), and violence in the press; and under intercultural information ethics—digital divide, and the ethical role of the Internet for social, political, cultural, and economic development. Many of the debates in information ethics, on these and other issues, have to do with specific kinds of relationships between subjects. The most important subject and a familiar figure in information ethics is the ethical subject engaged in moral deliberation, whether appearing as the bearer of moral rights and obligations to other subjects, or as an agent whose actions are judged, whether by others or by oneself, according to the standards of various moral codes and ethical principles. Many debates in information ethics revolve around conflicts between those acting according to principles of unfettered access to information and those finding some information offensive or harmful. Subjectivity is at the heart of information ethics. But how is subjectivity understood? Can it be understood in ways that broaden ethical reflection to include problems that remain invisible when subjectivity is taken for granted and when how it is created remains unquestioned? This article proposes some answers by investigating the meaning and role of subjectivity in information ethics. 2 In an article on cyberethics (2000), I asserted that there was no information ethics in any special sense beyond the application of general ethical principles to information services. Here, I take a more expansive view.
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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 | 0.024 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.075 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".