Inclusive Collecting as Polyphony: a Shared Collection of Works by Canadian BIPOC Composers in Academic Music Libraries
Bibliographic record
Abstract
A growing demand for BIPOC scores necessitated a collection assessment project undertaken at the University of Saskatchewan, from which, librarians determined that there was a need to acquire additional scores from “composers who identify as [Black, Indigenous, and People of Colour] BIPOC and Canadian, or who identify as BIPOC and are based in what is now known as Canada. (Doi, 2022)”. The dataset resulting from this assessment became the basis of what is now Canadian BIPOC Composers Shared Collecting Initiative (CBC-SCI). The Collections Committee of the Canadian branch of IAML began and implemented this initiative, focused on more inclusive representation within academic music libraries. The work relies on Canada’s robust inter-library loan network, providing access and visibility to Canadian BIPOC composers’ scores, and allows for extended support for libraries with smaller collection budgets. The CBC-SCI is now in its second year of operations, backed by participation from academic libraries across the country. We will present some of the outcomes from the project in its first years and summarize the initial assessment data to provide an overarching picture of how the project is progressing.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.025 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".