Academic Speech in Times of Political Disruption: Implications for Information Scholarship and Practice
Bibliographic record
Abstract
ABSTRACT This session aims to provide a space for collaborative, interdisciplinary inquiry into political and information environments and how information policies affect how people enact and experience vital issues such as public trust and freedom of speech. Issues related to information policy and research during times of political and social instability have had an ongoing presence at ASIS&T annual meetings and have provided a space for public discourse and debate. This session examines our roles as information professionals and scholars and our (increasingly complex) relations with public trust, political authority, electoral legitimacy, information access, privacy, and safety, as these are critically important values that underpin our raison d'être as scholars and educators. Yet, for many of us, they feel like “old news” in a political moment characterized by polarization, misinformation, data scraping, and surveillance, as well as a vilification of experts and science in popular discourse. As information scholars and professionals, we find ourselves struggling once again to address ethical and policy issues related to our labor and practice. The goal of this panel is to address the pressing issue of what constitutes intellectual freedom and the academic enterprise and political discourse in turbulent times. We seek to create a space to discuss the challenge of determining how and when to communicate with policymakers and the public directly in an inclusive way. The panelists will share their perspectives, and the remainder of the session will consist of discussions with the audience and the sharing of good practices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.119 | 0.165 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.038 | 0.063 |
| Scholarly communication | 0.074 | 0.049 |
| Open science | 0.006 | 0.028 |
| Research integrity | 0.018 | 0.019 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".