Bibliographic record
Abstract
In order to undertake liberatory memory work, engage effectively with communities and individuals, and centre people rather than records in their work, archival organizations must be aware of trauma and its effects. This article introduces the concept of trauma-informed practice to archives and other memory organizations. Trauma-informed practice is a strengths-based approach for organizations that acknowledges the pervasiveness of trauma and the risk and potential for people to be retraumatized through engagement with organizations such as archives and seeks to minimize triggers and negative interactions. It provides a framework of safety and offers a model of collaboration and empowerment that recognizes and centres the expertise of the individuals and communities documented within the records held in archives. Traumainformed practice also provides a way for archivists to practically implement many of the ideas discussed in the literature, including liberatory memory work, radical empathy, and participatory co-design. This article proposes several areas where a trauma-informed approach may be useful in archives and may lead to trauma-informed archival practice that provides better outcomes for all: users, staff, and memory organizations in general. Applying trauma-informed archival practice is multidimensional. It requires the comprehensive review of archival practice, theory, and processes and the consideration of the specific needs of individual memory organizations and the people who interact with them. Each organization should implement trauma-informed practice in the way that will achieve outcomes appropriate for its own context. These out comes can include recognizing and acknowledging past wrongs, ensuring safety for archives users and staff, empowering communities documented in archives, and using archives for justice and healing.
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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 | 0.023 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.015 | 0.040 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.002 | 0.034 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".