Models for Ethical Decision-Making for Use in Teaching InformationEthics: Challenges for Educating Diverse Information Professionals
Bibliographic record
Abstract
Teaching Information Ethics to a very diverse group of graduate students working towards careers as information professionals raises a number of challenges. The students come from different disciplines and a wide range of diverse educational, economic, social, and cultural backgrounds and from several different countries. At the University of Pittsburgh, students in the Information Ethics course are enrolled in one of three master’s or doctoral degree programs at the School of Information Sciences: information science, library and information science or telecommunications. In addition, graduate students, and an occasional senior-level undergraduate student, from other disciplines and schools, such as business, medicine, public and international affairs, as well as students from other universities, such as Carnegie Mellon University, take the fifteen-week course. Identifying and using models for ethical reflection and moral decision-making requires drawing on materials from several disciplines and adapting those models for the course. This paper will discuss some of the models used in the past, the advantages and disadvantages of the model currently used (i.e., Richard Paul and Linda Elder’s, The Miniature Guide to Understanding the Foundations of Ethical Reasoning. The Foundation for Critical Thinking, Dillon Beach, CA, 2003), and the evolution of the Information Ethics course over its fifteen-year history.
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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 | 0.117 | 0.147 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.008 | 0.036 |
| Scholarly communication | 0.028 | 0.028 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.011 | 0.018 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".