Innovations and Challenges in Teaching Information Ethics Across Educational Contexts
Bibliographic record
Abstract
Renewed attention to integrating information ethics within graduate library and information science (LIS) programs has forced LIS educators to ensure that future information professionals – and the users they interact with – participate appropriately and ethically in our contemporary information society. Along with focusing on graduate LIS curricula, information ethics must become infused in multiple and varied educational contexts, ranging from elementary and secondary education, technical degrees and undergraduate programs, public libraries, through popular media, and within the home. Teaching information ethics in these diverse settings and contexts brings numerous challenges and requires new understandings and innovative approaches. In keeping with the 2011 Association for Library and Information Science Education (ALISE) conference theme of “Competitiveness and Innovation,” a diverse panel of educators and researchers were convened to foster a discussion in how to best incorporate information ethics education across diverse contexts, and how to develop innovative educational methods to overcome the challenges these contexts inevitably present. This article reports on that panel discussion and offers recommendations towards achieving success in information ethics education.
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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 | 0.147 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.008 | 0.033 |
| Scholarly communication | 0.023 | 0.027 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.010 | 0.015 |
| 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".