Jennifer J. Davis - Colonial Reckoning: The Hidden History of the Census in France
Bibliographic record
Abstract
In this talk, Jennifer J. Davis, Associate Professor of History at The University of Oklahoma and coeditor of the Journal of Women’s History, will explore the roots of the modern census in France and the United States in a common document: a count of residents in colonial New France (Canada) in the year 1666. The practices that developed to track and tax the inhabitants in France’s American colonies contributed to durable categories of political inclusion and social discrimination. Davis will trace how religious categories informed racial categories in those records and examine long-term political resistance to enumeration and categorization of populations. She also will consider how race and religion factored in the most recent census data in the US (2020) and in France (2024). Laurie Ross, Professor and Director of the Department of Sustainability and Social Justice at Clark University, will provide commentary. This event continues the Roots of Everything, a lecture series sponsored by Early Modernists Unite (EMU)—a faculty collaborative bringing together scholars of medieval and early modern Europe and America—in conjunction with the Higgins School of Humanities. The series highlights various aspects of modern existence originating in the early modern world by connecting past and present knowledge. With thanks to the Department of History at Clark University for its generous support.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".