Case files, modernism and archival decolonization: the past, current and future management of case files by archives
Bibliographic record
Abstract
By using three case studies, this thesis explores the past and current practices, systems, and methods that archivists developed to manage, destroy, select, and make available case file records, as well as how current innovations are influencing a change in the management of case files today and into the future. I examine of the destruction of case files pertaining to the eugenics program of Alberta, the management of eHealth case files in Canada, and the National Centre for Truth and Reconciliation archive’s creation of “virtual case files.” I provide an overview of past and current practices utilized by archivists to manage case files from the mid-twentieth century to the early 2000s by discussing the archival literature surrounding the management of case files. I also outline the way that historians have used case files and why case files are important to historians. I explore the use of databases to manage case file records and the challenges that come with preserving these complex, interactive digital systems. I discuss the development of third order archival interface systems which would allow users to easily arrange archival digital records into as many different aggregations they need, as well as allow archivists to further contextualize and decolonize the records by placing the perspectives and needs of marginalized communities first in all archival decisions. Lastly, I argue that the concepts of imagined records, affect, and radical empathy should influence the decisions regarding the management of case files such as appraisal, retention, arrangement, description, preservation, and access.
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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 | 0.029 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.025 | 0.080 |
| Scholarly communication | 0.024 | 0.036 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.003 | 0.006 |
| 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".