“We Were Called Low-Grades” : Current Archival Approaches to the Digitization and Dissemination of Eugenics Collections
Bibliographic record
Abstract
This thesis examines the current methodological approaches to digital access and dissemination of eugenics archives. It looks in-depth at four institutions from around the world which provide some means of digital access to a specific eugenics collection that they control: the Wellcome Collection within the Wellcome Library in London, the Image Archive on the American Eugenics Movement founded in part by the Cold Springs Laboratory in New York, the Canadian Eugenics Archive, and the State Institute for Race Biology at the Uppsala University Library Special Collections in Sweden. The complexity of archival methodology and thought has evolved over the course of the past few decades, with more and more institutions recognizing the historical bias of their collections and many working towards combating these biases to bridge knowledge gaps and provide more detailed and nuanced understanding of their materials. With the development of the internet, and the pressure for heritage institutions to turn towards more digital methods of dissemination of their collections, there has been fierce debate as to good practices towards implementing these digital means of access and dissemination. Collections which contain historically problematic materials, such as eugenics collections, can make digitization and digital methodologies particularly difficult. This thesis serves as a groundwork for the development of good institutional methodologies in terms of digitization of eugenics-related materials by comparing the available methodologies employed by four institutions holding materials which were particularly significant during the period of legally applied eugenics on their respective populations.
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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 | 0.118 | 0.190 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.023 |
| Science and technology studies | 0.016 | 0.070 |
| Scholarly communication | 0.055 | 0.040 |
| Open science | 0.008 | 0.027 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".