The Language of Cataloguing: Deconstructing and Decolonizing Systems of Organization in Libraries
Bibliographic record
Abstract
This paper analyzes the language of cataloguing because the information that librarians and other information professionals provide to others has a huge impact both on how others are viewed and how others view themselves. This ultimately comes down to the way in which words are given meaning and interpreted according to the socio-political climate of the time. As society, politics, and economies change, so too does the language of representation. Therefore, the Library of Congress subject headings (LCSH) as a system of categorization is only as effective as the language that is used to define what is and what is not. Moreover, those who control the language of categorization control access to the information categorized within that system. Consequently, librarians must always be critical of the language they are using in their information organization systems. Language is continuously evolving according to societal discourse and politics; therefore, if libraries are to maintain their social responsibility to provide information to all, including socially disadvantaged and marginalized peoples, then librarians must continuously advocate for changes to subject headings. Librarians must also recognize and reflect on their own internal biases when cataloguing and make it their job to deconstruct language and decolonize the systems that perpetuate the continued marginalization of others. To remain neutral about these systems is the very opposite of what it means to be a librarian in the twenty-first century.
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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 | 0.029 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.016 | 0.114 |
| Scholarly communication | 0.025 | 0.029 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".