Interrupting the Criminalization of Information in the Academic Library Classroom
Bibliographic record
Abstract
Libraries have long been involved in conversations around book banning and censorship. The American Library Association noted that librarians and information workers in 2022 witnessed the most documented attempts at banning books ever recorded. This is in lockstep with contemporary examples of legislative efforts to censor, ban, and by extension criminalize information. The criminalization of information is one that has a direct impact on library users as well as academic freedom. In an effort to best support scholars at all levels in the University, academic teaching librarians will need to develop strategies to approach the information classroom. Understanding that book bans and censorship are a form of criminalization allows us to be in conversation with scholarship that focus on how to combat criminalization such as abolitionist pedagogy. This article introduces core concepts of abolitionist pedagogy as a means to create new educational justice pathways and to interrupt information criminalization.
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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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.025 | 0.066 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.004 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 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".