Bibliographic record
Abstract
Co-sponsored by the Information Policy and Information Ethics special interest groups (SIGs), this proposal is for a pair of traditional 90-minute speaker panels, facilitated by the respective SIG convenors and supporting interlocutors, which will address the contested nature of the public in policy and implications for educators, including the ALISE community. The first co-sponsored panel explores the past decade’s ramped-up effort among activists to defund or use punitive measures against public institutions that support and enact diversity, equity, inclusion, and decolonization (DEID) efforts. For example, many libraries have purposefully endeavored to increase the diversity of their collections and programs with respect to BIPOC and LGBTQ+ authors, speakers, and themed displays. Many school systems have invested in DEID curriculum and resources to better serve their students, and universities have invested in staff and faculty development to support students, staff, and faculty of color. However, some community members and politicians are opposed to any DEID efforts and thus seek to eliminate them, using either legal or punitive financial means. This panel will give attention to the historical context of DEID efforts and current implications for public institutions; lobbying; advocacy work in public institutions; and legislation and states/provincial/territory rights and federalism. The Information Policy SIG convenor, with the support of interlocutors, guides the audience in a critical discussion of implications for multiple constituent groups (e.g., library users, people working within library and information institutions, trustees, mayors, legislators, police and security workforce, journalists). At the close, participants are encouraged to surface connecting topics and potential conversationalists for ongoing engagement co-hosted by the Information Policy and Information Ethics SIGs. The second co-sponsored panel pivots attention to instances of censorship and self-censorship among some of the people who work in higher education and K-12 public educational institutions and their libraries therein in various national contexts. The topics covered in this panel include the effect of censorship and self-censorship on teaching, learning, and curriculum; the contested nature of intellectual freedom, represented through specific cases of censorship, challenge and critique; implications for ethical AI in research and higher education; workplace expression, and employer; rights and responsibilities of academic freedom; and the use and abuse of civility codes. The Information Ethics SIG convenor, with the support of interlocutors, will guide the audience in a discussion of implications for the education of library and information professionals and those individuals who are engaged in teaching them. This includes critical discussion with respect to: employees and scholars at risk (e.g., equity deserving, precariously employed or under-employed, international work); threats on tenure and tenured members of academic staff (e.g. faculty, archivists, librarians); bargaining and job action; curtailing of academic freedom; declining collegial/shared governance in higher education; the importance of strong administrative and trustee support in our educational institutions; and related legislative priorities. At the close, participants are encouraged to surface connecting topics and potential conversationalists for ongoing engagement co-hosted by the Information Policy and Information Ethics SIGs.
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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 | 0.057 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.020 | 0.056 |
| Scholarly communication | 0.061 | 0.034 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.016 | 0.017 |
| 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".