‘I can’t even read straight’: Exploring the influences on LGBTQ+ library collections through an artificial-intelligence-mediated parallel-synthesis-scoping-review approach
Bibliographic record
Abstract
A small but mature body of literature around LGBTQ+ library collections is available to researchers and practitioners. Using a novel method – the parallel synthesis scoping review – the authors have incorporated artificial-intelligence-enabled topic modelling into the traditional scoping review method to explore the underlying factors influencing the collection of LGBTQ+ materials in libraries. This review was supported by a systematic scoping search of five databases (Library, Information Science and Technology Abstracts; Scopus; MEDLINE; Embase; Cumulative Index of Nursing and Allied Health Literature), with blinded screening and data extraction. Parallel synthesis led to a framework charting stakeholders against an Outreach ↔ Censorship Continuum. It includes 16 forms of censorship and outreach, and 8 underlying influences that encourage behaviours towards either censorship or outreach. The authors further find that the framework is a manifestation of a struggle between two competing visions of safe spaces, in which librarians have used many strategies to resist censorship and ensure that their collections provide a safe space for LGBTQ+ library patrons.
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 | 0.241 | 0.461 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.017 | 0.025 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.018 | 0.019 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".