Integrating Equity and Reconciliation Work into Archival Descriptive Practice at the University of Waterloo
Bibliographic record
Abstract
Despite sustained calls for a critical review of harmful content within archival descriptive records, there remains much to be explored by way of implications for Canadian academic archives. This article addresses the absence of Canadian archival practitioners in broader discussions about the revision and remediation of descriptive records by exploring how staff in Special Collections & Archives at the University of Waterloo Library are working to integrate equityand reconciliation-informed thinking into the department’s archival practice by revising their approach to language in archival descriptions. Beginning with an overview of the department and the landscape in which it operates, this article provides a brief review of guidance available in the Rules for Archival Description. It then provides the rationale behind the recent changes to descriptive practice before exploring a series of examples of how and where this work is newly underway. The article concludes with a consideration of current identified challenges and the related work ahead.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".