Library Response to Black Liberation Collective: A Review of Reference Services Review Student Calls for Change & Implications for Anti-Racist Initiatives in Academic Libraries
Bibliographic record
Abstract
This article examines seven case studies concerning college libraries addressing demands collated by the Black Liberation Collective in 2015. Six years out from the publication of the lists, we evaluate statements issued by the libraries and posted on their websites, the promises that have been made to address inequities, and the ensuing actions the libraries have taken to create a welcoming, inclusive community. In solidarity with the protests’ student activists at universities across the United States and Canada organized into the Black Liberation Collective and held the first #StudentBlackoutOut day of protests on university campuses on November 15 followed by the publication of lists of demands to over 80 colleges in 28 states, the District of Columbia, and Canada in the hopes of creating more-equitable and inclusive institutions. Seven academic libraries in particular were included with demands to better serve the Black Indigenous People of Color (BIPOC) community. Through this investigation, we examine the responses from these libraries and recommend best practices for evolving academic libraries to serve BIPOC students.
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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 | 0.042 | 0.126 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.022 | 0.024 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".