Pursuing Equity, Diversity, and Inclusion in Collection Development
Bibliographic record
Abstract
The Textile Museum of Canada (the Textile Museum) is pursuing institutional change with a goal to meaningfully address and redress absences in its permanent collection of over 15,000 textiles. To support this goal, the Textile Museum developed a Collection Development Plan guided by emerging best practices supporting Equity, Diversity, and Inclusion in museums, research into the institution’s historical and existing collecting practices and policies, and focus group sessions and interviews with members of multiple communities. These actions led to a series of recommendations for the Textile Museum to implement. Being represented within the Textile Museum’s collection matters to people, and our findings encourage the institution to value intangible heritage (not only material objects), and to care for collections in ways that enliven them through connections with people in storage, exhibition, and digital spaces. This case study presents the origins, process, and outcomes of this ongoing work to institutionalize new practices and imperatives holistically across departments.
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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 | 0.033 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.038 | 0.061 |
| Scholarly communication | 0.022 | 0.010 |
| Open science | 0.002 | 0.040 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".