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Valuing Historical Land Claims and Its Loss of Use

2013· article· en· W1135412266 on OpenAlexaff
Fred Lazar, Eliezer Z. Prisman

Bibliographic record

VenueJournal of Real Estate Literature · 2013
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing Market and Economics
Canadian institutionsYork University
Fundersnot available
KeywordsArbitrationAsset (computer security)EconomicsCompensation (psychology)Government (linguistics)Law and economicsActuarial scienceLawPolitical scienceComputer science

Abstract

fetched live from OpenAlex

Historical land claims commonly arise in disputes between government and the former inhabitants of the land. These claims typically relate to a past damage caused either by inadequate compensation or by inappropriate taking of the land. There are opinions voiced in the literature that these claims should be valued differently by the prospective and retrospective approach, respectively. This paper shows the deficiencies and perverse motivation for claims including the optimal timing of the claim induced by the retrospective approach. A new way of valuing the damage that is in the spirit of the prospective approach rooted in modern financial theory and option pricing is suggested and justified. This approach alleviates some deficiencies and spares the need for a long sequence of historical asset prices that are not readily available. Hence, it makes settling such disputes easier and increases the likelihood of arbitration instead of costly court cases.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.024
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.003
Scholarly communication0.0040.008
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.019
GPT teacher head0.200
Teacher spread0.181 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2013
Admission routes1
Has abstractyes

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