Bibliographic record
Abstract
Abstract This book reacts to an economic literature which relies on theory to explain property rights. To correct this truistic approach the book traces the actual evolution by examining when, where, and how particular rights have acquired one or more of six characteristics. Coverage begins, for most resources, with rights acquired in the Norman Conquest, and examines their evolution in England and the US, as well as Canada and Australia. It is emphasized that it was the accumulation of these characteristics that made property rights more ‘complete’. The book is divided into four main parts. Each part surveys the history of individual rights held by owners and users of one of four natural resources: flowing water, fisheries, minerals, and timber. Coal and petroleum rights get their own chapter. Ocean fisheries' rights are an example. For centuries fishing had been ‘common property’. Government reinforced a public right of fishing with such enactments as Magna Carta. Nevertheless, since the nineteenth century most fishermen's rights have obtained some of the exclusivity characteristic. The book identifies episodes featuring a ‘demand’ for increases in a right's characteristics or/and a increase in ‘supply’ by courts, government, and custom. Half the resource chapters are devoted to rights to use public (Crown) lands; half are devoted to private rights on private lands.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 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".