Development of the trauma-informed archival practices scale
Bibliographic record
Abstract
In response to the growing awareness regarding the potential for emotional trauma in both archivists and members of the public (donors, users, and community members) who interact with records of human suffering and atrocity, there has been a call for the implementation of trauma-informed practices in archival organizations. Several prominent researchers and theorists have suggested policy and practice elements that would support trauma-informed archives based on principles of transparency, empathy and respect; survivor-centered approaches; and creating a culture of caring. While these contributions have been critical to a shifting paradigm of archival practice, to date there is no tool to measure the degree to which organizations have enacted such approaches. A quantitative approach to measuring practices could complement existing qualitative scholarship, documenting progress in this area and supporting research to evaluate whether these practices, if implemented, lead to better outcomes. This study describes the development and evaluation of the Trauma-Informed Archival Practices Scale which contains items derived from the scholarly literature and previous research of the developers. The tool was distributed to archival institutions across Canada, and a factor analysis was conducted with the resulting sample of 167 organizations. The total scale and four subscales related to users, donors, community members, and staff demonstrated adequate reliability and theoretical congruence. The resulting tool may thus be a useful addition to current approaches to research on traumatic aspects of archives and models for ameliorating potential impacts on researchers, donors and archivists.
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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 | 0.017 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".