Remembering Forgotten Stories in the Archives
Bibliographic record
Abstract
Out of approximately 10 km of research material in the Queen’s Archives, how do you remember just one person, especially when the archival records are often fragmented? As HIST 502 Interns for the 2022 Fall Semester, we dived into this idea, aiming to rediscover (and, through digitisation, publicly promote) the life and works of Dr. Allie Vibert Douglas, a Canadian astronomer, physics professor and former Dean of Women at Queen’s University. In our 4-month research period, we made a number of fascinating discoveries in her fonds that we would like to bring to this conference. First, we learned how easily misinformation can pervade archival research with incomplete or biased documentation, in addition to the personal bias brought by the researchers themselves. As the first female astrophysicist in Canada, her archival material gave us insight into how embedded gender bias was in the academic world. After receiving an email from her living relatives, we were able to learn firsthand the impact archival research can have in the contemporary world. As well, we found a surprisingly high emotional impact from our readings in the archives, as we began to feel that a relationship had developed between us despite all the time that has passed between our lifetimes. Our research brought us to the conclusion that while the archives can promote misinformation, it is also an essential tool to combat it. Archives provide a snapshot of someone’s life on paper, allowing the opportunity to rediscover history through the words of who lived it.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.021 | 0.027 |
| Scholarly communication | 0.024 | 0.030 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.005 | 0.009 |
| 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".