Something Old, Something New, Something Borrowed, and Something Due: Using Social Media Stories to Decolonize Rare Books and Special Collections
Bibliographic record
Abstract
The University of Winnipeg campus is located in Treaty 1 territory.The land on which we gather today is the traditional territory of Anishinaabeg, Cree, Oji-Cree, Dakota, and Dene Peoples, and the homeland of the Métis Nation.We honour, recognize and respect these nations as the traditional stewards of this land. WHITE INSTITUTIONAL PRESENCEWhen cultural practices associated with "whiteness" are normalized and considered the standard or expected behaviour in academic settings.(Gusa, 2010 as explained by Pashia, 2017).What values do we use to organize information?Cataloguing Is our instruction inclusive? Information literacyWho are the authors/publishers in our collection?What stories are being told? Collection developmentHow easy is it to access our collections? Access Systems of oppression in libraries CataloguingClassification systems do not account for intersectionality.Furthermore, they establish normal-other relationships based on gender, race, nationality, etc. ( Crowe & Elzi, 2017, p.276-7).
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.007 |
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".