WHAT HAVE AMERICANS (AND MAYBE THE REST OF THE WORLD) PAID FOR NOT HAVING A PUBLIC PROPERTY RIGHTS INFRASTRUCTURE?1
Bibliographic record
Abstract
The United States of America, unlike most of the developed countries, does not have a public federal or state property rights infrastructure. In an article written in 2002 and titled «What do Americans pay for not having a public land registration system?», Mr Bengt Kjellson estimated the costs of this weakness in the US economy at $20 billion annually (Kjellson, 2002). In the new context of the mortgage crisis in the USA and the economic crisis it has triggered worldwide, we can reformulate the question this way: «What have Americans (and maybe the rest of the world) paid for not having a public property rights infrastructure?». In effect, we believe that a good property rights infrastructure could have mitigated the effect of the land market crisis and thereby avoided the loss of many hundreds or even thousands of billion dollars. This paper indicates that the lack of a sound property rights infrastructure in the USA has contributed to the collapse of its land market. Of course, this is not the only cause of the mortgage crisis. The negligence of the government to control the banking system and the fact that banks have been too loose in their loan controls is obvious. But in crisis times, good, reliable, and accessible information available on time is of critical importance. When this information is missing or hard to obtain without any guarantee of reliability the crisis will become like a storm in the warm waters and it becomes a hurricane. And this is what happened last year in the USA. In its inauguration speech the US President Barack Obama said «Starting today, we must pick ourselves up, dust ourselves off, and begin again the work of remaking America» 2 . So, why not remake America and its land market on more sustainable basis?
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".