Teaching Information Ethics in Higher Education: A Crash Course in Academic Labour
Bibliographic record
Abstract
This article builds on several prior informal publications that delve into my experiences teaching a course on intellectual freedom and social responsibility in librarianship in the context of the North American library and information studies curriculum. Here, I extend those discussions into a deeper exploration of the academic labour that frames conditions for teaching information ethics. While the intellectual freedom and social responsibility in librarianship subject matter represents only one narrow slice of the bigger information ethics pie, the actual teaching of it sheds light on more universal instructor immersion in contestations over internationalization of higher education, the contingent worker model, the meaning of global citizenship education and research, and academic freedom in the 21st century. This focused lens takes in how the working conditions of faculty are the learning conditions of students, as well as how some of the ill practices explored in information ethics (e.g., censorship) can also be apparent in the institutions in which it is taught. Thus, this article recognizes the political context of information ethics within the academy, a place undergoing redefinition in academic visions and plans designed to push faculty, staff and students harder in global competitions for university rankings.
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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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.025 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.002 | 0.023 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.012 | 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".