Bibliographic record
Abstract
This book has been almost two decades in the making, developing from a master's memoir through various conference papers, then emerging as a phd dissertation, dismantled and refitted during postdoctoral revisions and reconstructed in the course of further conference papers and articles, and finally appearing as this monograph.The resulting narrative is in part an experiment to integrate two census-based approaches to understanding historical patterns: quantitative analysis of historical census data to discern the behaviour of ordinary persons and discourse analysis of census texts to explore the representation of both census concepts and census respondents.My attempt to combine these two approaches is inspired by the work of Canadian and US researchers, and I have had the good fortune to learn from and work with several of these scholars in both countries.Professors, mentors, colleagues, friends, and family have all provided the immeasurable support needed to initiate and sustain this project over the years.The Department of History at the University of Minnesota provided an exceptional academic environment in which to learn the skills of a historian.My phd advisor, Steven Ruggles, guided me through the process of researching and writing a doctoral dissertation, providing frank criticism of my research design and sharing in my enthusiasm for the results.His consistent encouragement, optimism, and good humour made graduate school enjoyable.As a research assistant with the Minnesota Historical Census Projects, I not only learned skills in processing and interpreting census data but also benefited from the uncommonly friendly and stimulating environment of the census research assistant office, a room that served as an intellectual and emotional home-on-campus during my five years at Minnesota.Remembering
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.251 | 0.178 |
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".