Bibliographic record
Abstract
A theoretic model is developed to analyse strategic leasing behaviours of U.S. landowners in a non‐exclusively owned common oil pool. Oil field development is modelled as a simultaneous‐move two‐stage non‐cooperative game. The landowners choose leasing strategies in the first stage; independent lease operators choose extraction strategies in the second. Ownership structure is distinguished from operation structure and their relationship analyzed. It is shown that, in a non‐exclusively owned oil field, it is individually rational for a landowner to subdivide his landholding and delegate production rights to multiple independent firms, although more dispersed production control leads to heavier common pool losses. JEL classification numbers: L23, D23, Q24 Diviser pour régner : baux multiples dans des champs pétroliers construits sur une même nappe. Ce mémoire construit un modèle théorique pour analyser les comportements stratégiques des propriétaires terriens dans le cas de baux de forage quand le fond pétrolier à exploiter est en propriété commune. On modélise le processus de développement du champs pétrolier comme un jeu sans coopération à deux étapes avec décisions simultanées. Les propriétaires terriens choisissent des stratégies de baux dans une première étape et des opérateurs locataires indépendants choisissent des stratégies d’extraction dans un second temps. Ce faisant, on est capable de distinguer la structure de propriété de la structure des opérations, et d’analyser les relations entre elles. On montre que pour une nappe pétrolière qui est en propriété commune, il est rationnel pour un propriétaire terrien individuel de subdiviser sa propriété et de louer des droits de production à plusieurs entreprises indépendantes, même si cette dispersion du contrôle de la production entraîne des pertes beaucoup plus lourdes dans l’exploitation de la nappe pétrolière en propriété commune.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".