Bibliographic record
Abstract
Much has been written and discussed about artificial intelligence (AI) and growing sentiment suggests it is here to stay. How AI should be used, positioned, developed and governed? Will AI be the solution to persistent and inconceivable challenges, position early adopters for competitive advantage and economic growth? Questions and concerns abound, but it is time we move beyond debate and come to a resolution regarding ethical AI standards and policies to influence and govern use. Co-sponsored by the Information Policy and Information Ethics special interest groups (SIGs), this proposal is for a pair of 90-minute speaker panels, facilitated by the respective SIG convenors. This joint panel presents a continuous conversation to strengthen our resolve for ethical AI standards and policies. Panelists will present intercultural and geopolitical perspectives to frame an ethical stance that will be workshopped across panels for an ethical pedagogical position to inform policy. The second panel, Implementing AI in pedagogy: Toward a framework for policy development, will feature four speakers focusing on policy considerations. Shengnan Yang (University of Western Ontario) and Awa Zhu (University of Tennessee, Knoxville) will share a study examining the contradictions that emerge when Generative AI is integrated into LIS teaching using Activity Theory, exploring how faculty navigate tensions between pedagogical values and technological innovation. Jenny Bossaller (University of Missouri) will discuss the shifting U.S. policy on AI, from Biden’s cautious BluePrint for an AI Bill of Rights to Trump’s stance, marked by laissez-faire and rapid deployment. That shift has global repercussions for both higher education and scholarly publishing. Adam Berkowitz (University of Alabama) will speak on the legal frameworks that govern intellectual property, data, non-expressive works, and fair use, which enable tech companies to leverage copyrighted works as AI training data, and ethical, critical, and legal implications concerning the manner in which tech companies extract data from copyrighted works and the use of AI to produce expressive works. We acknowledge and appreciate the individual and collective decolonizing efforts and commitments of our SIG members. Our conversations reflect complex intercultural challenges, which we discuss with an ethic of care, confidentiality, intellectual curiosity and respect for divergent perspectives and practices.
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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 | 0.035 | 0.140 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.014 |
| Scholarly communication | 0.031 | 0.029 |
| Open science | 0.007 | 0.021 |
| Research integrity | 0.033 | 0.032 |
| Insufficient payload (model declined to judge) | 0.114 | 0.046 |
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".