Adapting Public Library Knowledge Organisation for Diverse Communities
Bibliographic record
Abstract
As knowledge organisation systems in public libraries are designed to be controlled and consistent, they struggle to keep pace with the needs of diverse and changing communities. With a theoretical basis in post-structuralism, this literature review explores the ways that adaptability can be built into these rigid systems to appropriately honour community truths and create more useful and welcoming collections. Three broad avenues of inquiry are presented. Librarian-led adaptability explores librarian training and initiatives. Tools like folksonomy and crosswalks are suggested to augment current systems. Finally, historical, international, and critical adaptations to Dewey Decimal Classification (DDC) are discussed. The field of critical classification overwhelmingly centres on knowledge organisation in academic libraries, leaving a gap in the literature related to public libraries and DDC adaptations focused on equity, diversity, and inclusion. This review seeks to prove this topic’s merit for more rigorous study and calls for the strengthening of a community of practice.
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 | 0.025 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.017 | 0.020 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.002 | 0.002 |
| 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".