Distributed labour: managing harmful language work in a Canadian library partnership
Bibliographic record
Abstract
A major reason for the prevalence of harmful language in library catalogs is the hegemony of the Library of Congress. Recent threads in the AUTOCAT listserv show the resistance of catalogers to include their own voices, let alone the voices of marginalized groups that have been underrepresented in the profession, throwing their hands up and saying I’m just a cataloger, we have to follow the established rules, etc. At the same time, metadata staff have been cut from many university libraries, leaving those who are interested in doing metadata justice work overwhelmed. In order to address some of these challenges, Ontario Council of University Libraries (OCUL) Collaborative Futures (a shared library platform group) created the Decolonizing Descriptions Implementation Working Group to manage harmful language across the Collaborative Futures partnership. As members of this group, we would like to discuss our efforts to manage alternative vocabularies in an Alma network zone environment, and some of the issues and crossroads we have faced thus far. Our current approach is to replace and/or amend LCSH terms with other, already established vocabularies like Manitoba Archival Information Network Indigenous Subject Headings, Saskatchewan Indigenous Subject Headings, Canadian Subject Headings, Canadiana, and Homosaurus, but this may evolve over time. We will present what our partner libraries have been working on individually and our working group’s efforts to centralize efforts and possibly implement a distributed labor model in OCUL CF. We are a nascent group and will be seeking feedback from colleagues.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: yes | Not applicable | low |
| gpt | no category Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.021 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.061 | 0.018 |
| Scholarly communication | 0.023 | 0.015 |
| Open science | 0.007 | 0.037 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".