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Record W2927965397 · doi:10.1017/s1744137419000110

Rationing by racing and the Oklahoma land rushes

2019· article· en· W2927965397 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Institutional Economics · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicExperimental Behavioral Economics Studies
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsEconomic rentCompetition (biology)LotteryRationingEconomicsQuality (philosophy)Land ValuesLand useProperty rightsSimple (philosophy)Natural resource economicsMicroeconomicsCivil engineeringEconomic growthEngineeringEcology

Abstract

fetched live from OpenAlex

Abstract Yoram Barzel was always aware that competition is ubiquitous and takes many forms, and he was among the first to analyze settings where individuals compete on the basis of time, rather than price. This paper applies his insights to study the Oklahoma land rushes, where thousands of individuals raced to establish property rights to land. A simple modification of Barzel's analysis generates a model of rationing by racing, and we test its predictions using new data on the timing and location of over 73,000 homestead claims within the five distinct land rushes and one lottery. We find that increases in land quality or decreases in the cost of racing generate corresponding increases in the equilibrium speed, implying that potential rents are dissipated by investments in speed. The analysis highlights the lasting significance of Barzel's insights regarding non-price competition.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.776
Threshold uncertainty score0.349

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.012
GPT teacher head0.266
Teacher spread0.254 · 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