Review of <i>Keepers of the Record: The History of the Hudson'sBay Company Archives. </i> By Deidre Simmons
Bibliographic record
Abstract
Given the remarkable character of the Hudson's Bay Company Archives, it is a little hard to believe that this is the first comprehensive study of their history. It was worth the wait, though, as Simmons deftly weaves more than three centuries of people and paper into a narrative as captivating as the records themselves. As Simmons observes in her introduction, this book is much more than just the history of the HBCA as an institution: it is both a history of the HBC's record-keeping (and record-keepers) from its earliest days and a case study in British and Canadian archival history. Although Simmons occasionally struggles with the task of placing the company and its operations in their broader historical contexts, her grasp of its internal workings is strong. She illustrates her examination of early recordkeeping with discussions of the motives and people behind the paperwork: her detailed look at clerk Samuel Hopkins (fl. 1715-31) is particularly informative. The reforms and reorganisations of the nineteenth century are given a new perspective as part of a more "modern" company's accounting and reporting systems. New light is also shed on the company's transition from fur trade to settlement, and the several bureaucratic changes that entailed.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.011 | 0.016 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".