Bibliographic record
Abstract
Academic library collections serve each broad discipline as either the raw material for research or the overarching structure that places its scholarly production in context. Within the humanities, the raw materials of research that are available to scholars are often biased towards a Western literary canon. This is especially true of the digital humanities where scholarship can only reflect what it is possible for scholars to see. Historian Kim Gallon (Making a Case for the Black Digital Humanities, 2016) describes a digital humanities that frames human culture and society through digital library collections that lack equity in their representation of Black experience. Black studies, she argued, cannot leverage the opportunities digital humanities affords if this problem with digital collections remains. This poster will first present an overview of the methods library staff used to assess the digital collections at a large Canadian university library to identify the gaps that exist in its holdings of the literature of Black Canadians. Next this poster will describe how these gaps need to be addressed both through traditional commercial acquisition and through direct partnerships with publishers and authors to ensure this important work can be digitized, preserved, and shared.
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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.015 | 0.018 |
| Science and technology studies | 0.058 | 0.011 |
| Scholarly communication | 0.020 | 0.005 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.049 | 0.004 |
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".