Bibliographic record
Abstract
To our knowledge, there are limited roles in Canadian academic libraries whose sole focus is on Equity, Diversity, Inclusion and Access (EDIA). While libraries in the U.S. are in a political struggle over EDIA and face efforts to remove books and threaten their operations in Canada we should be keeping a close eye on these challenges. Building a sustainable resistance to these kinds of efforts requires engaged communities and a dedicated role to educate and advocate for social issues such as the environment, race, gender, disability and access to resources. People should be able to go to libraries and find texts that represent their identities and search for information in the archives that do not cause further harm. In the short time in our roles, we would like to highlight the importance of some of the work that we have undertaken and to emphasize a call to action for more Canadian academic libraries to create these types of positions. The realization and theory that has been developed from professional experience in an institutional EDI office, is that having this specialized role better positions the library to hire, retain and diversify its staff, coordinate customized training and resource sharing, and generally support a culture of inclusion more effectively than if these roles were not present. However, this work is not without its challenges, and we would be remiss if these were not mentioned. Participants will hear honest accounts from two EDIA leaders.
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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.060 | 0.018 |
| Scholarly communication | 0.026 | 0.006 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.019 | 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".