An Analysis of Anti-Fat Bias LibGuides: Are Libraries in the Thick of It?
Bibliographic record
Abstract
Objective – This research investigates library research guides that share information about anti-fat bias to support weight-inclusive education or practice. By analyzing these guides, we seek to understand how academic librarians are engaging in this work and how they can continue to support weight inclusivity as educators, proponents of information literacy, and interdisciplinary partners. Methods – The authors searched for and screened publicly available LibGuides from academic libraries that included content about anti-fat bias, weight stigma, and/or body liberation. Relevant guides were then evaluated with an original framework to examine their content for insight about their target audience and context. Results – The authors identified and analyzed 36 relevant LibGuides, predominantly from college and university libraries. Thirty-three LibGuides came from institutions in the United States, and most of the institutions had at least one health sciences program, though eight offered no health-related programs. Thirty-two of the analyzed LibGuides presented anti-fat bias content in a tab within a larger guide, while the remaining few were standalone guides. The majority of guides with tab-level anti-fat bias content presented it as a social justice issue, though a few framed the content in a nutrition or other context. The most popular resource types offered in the guides were books, popular articles, videos, associations/organizations, and academic articles. Conclusion – Weight inclusivity discourse is growing across disciplines and is an area that librarians are well-situated to support. Presenting anti-fat bias as a social justice and diversity, equity, inclusion, and accessibility (DEIA) issue in libraries is promising and highlights library workers’ commitment to anti-oppression efforts and learning. Work remains to be done to integrate more anti-fat bias content into academic curricula and education, and librarians should look to engage with disciplinary educators, learners, and colleagues to grow and support this work, particularly in the context of the health sciences.
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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 | 0.017 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.019 | 0.029 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".