Measuring Away the Importance of Institutions: The Case of Seigneurial Tenure and Agricultural Output in Canada East, 1851*
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
Abstract This article argues that the 1851 census of Canada East (the modern‐day province of Quebec) requires a set of important corrections. Using corrections based on ethnic origin composition, I demonstrate how significantly wheat and oat yields were underestimated in Canada East. More importantly, I argue that the measurement errors are not randomly distributed and that they are biased against attempts to test the role of institutions. I show how the new method of correcting the data change our interpretation of agricultural efficiency in Lower Canada in the mid‐19th century. While this correction may seem minor, it shows that in the past, the data took a form that was biased against numerous hypotheses concerning land tenure institutions.
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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 | 0.001 | 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 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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