Large Scale Institutional Changes: Land Demarcation Within the British Empire
Bibliographic record
Abstract
This paper examines the economics of large scale institutional change by studying the adoption of the land demarcation practices within the British Empire during the 17th through 19th Centuries.The advantages of systematic, coordinated demarcation, such as with the rectangular survey, relative to individualized, haphazard demarcation, such as with metes and bounds, for reducing transaction costs were understood by this time and incorporated into British colonial policy.Still, there was considerable variation in the institutions adopted even though that the regions had similar legal structures and immigrant populations.We study the determinants of institutional change by developing an analytical framework, deriving testable implications, and then analyzing a data set that includes U.S., Canadian, Australian, and New Zealand temperate colonies using GIS data.We find that a simple framework that outlines the costs and benefits of implementing the demarcation systems can explain the different institutions that are observed.Once in place, these institutions persist, indicating a strong institutional path dependence that can influence transaction costs, the extent of land markets, and the nature of resource use.The agricultural land institutions that we examine remain in force today, in some cases over 300 years later.In this regard, institutions of land are durable, much as are other institutions, such as language and law.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".