CAML Collections Committee BIPOC Canadian Composers Shared Collecting Plan
Bibliographic record
Abstract
Building on the BIPOC Canadian Composers project (Doi & Hilts, 2022), the CAML Collections Committee has begun to develop a plan to systematically collect the works of BIPOC (Black, Indigenous, and People of Colour) Canadian Composers in a collaborative and coordinated approach. This presentation will outline the details of this shared collecting plan, and will present an opportunity for CAML members to share feedback and ask questions. This presentation will outline the work of the committee members including identifying purchasing options, conversations with vendors, and plans for rolling out a national collaborative collecting plan. We will discuss some of the challenges that have arisen, and how this project ties in to wider inclusive collecting objectives at our own institutions. As part of this presentation, the Collection Committee will be seeking input from individuals or institutions that might be interested in volunteering to commit to collecting the works of individual composers from the BIPOC Canadian Composers list. We view systematic collecting work of this kind as one of many tools to ensure diverse representation within Canadian library systems, especially music collections. This project has potential to serve as a stepping stone to testing future possibilities for systematic collecting to achieve broader coverage of Canadian musical works. The long-term goal of collaborating with Canadian music score vendors and universities to develop a formal shared collection plan is one way to ensure comprehensive collecting and preservation of works by BIPOC composers across Canada.
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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 | 0.034 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.028 | 0.004 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.066 | 0.024 |
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".