Historians, archives, and the stories we create
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Historians, archives, and the stories we create Learning about history through archives can help historians expand the way they acquire and interpret information. Cecilia Morgan, from the University of Toronto, discusses how archives have influenced her own research. All historians have archive stories, although not all historians experience the archives in quite the same way. So, it’s fitting to start this article with the oft-quoted line from fellow Canadian Joni Mitchell’s song, Big Yellow Taxi: “You don’t know what you’ve got ‘till it’s gone.” Or, in my case, not so much gone as (fortunately temporarily) inaccessible. From March 2020 until the spring of 2022, the pandemic caused by the COVID-19 virus closed the two university archives that hold the two large collections of family correspondence on which my research project – a study of two middle-class settler families in nineteenth-century Ontario – depends.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it