Access Denied: Defending Research in a Shrinking Scholarly Landscape
Bibliographic record
Abstract
In early 2025, the U.S. Department of Education announced a reduction in the indexing of key journals in the ERIC database. As government censorship increasingly threatens the accessibility of educational research, librarians are emerging as critical defenders of open access and research transparency. This presentation reports on the proactive efforts of a Canadian academic librarian who developed actionable workarounds to address the disappearance of articles from ERIC. These missing records pose a significant challenge to researchers conducting knowledge syntheses, who face a reporting and reproducibility crisis when search results fluctuate or shrink without explanation. The implications are profound: systematic reviews may be compromised, and the scholarly record distorted. To mitigate this loss, this presentation will highlight methods to preserve the scholarly record, even when external forces alter or restrict access to previously available research. The session will also explore cross-disciplinary partnerships and initiatives that librarians can adapt to strengthen their advocacy and technical responses. Finally, the presentation will critically examine the ongoing corporatization of information once considered freely accessible. As proprietary interests increasingly shape the availability of research data, librarians must confront a growing unknown: how to safeguard open knowledge in an era of privatized access. This session invites researchers, librarians, and concerned citizens to collaborate on defending the foundations of scholarly inquiry.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.009 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.019 | 0.037 |
| Open science | 0.008 | 0.006 |
| Research integrity | 0.001 | 0.011 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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; both teacher heads agree on what is shown here.
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".