Bibliographic record
Abstract
Abstract We investigate a mixed market where a state‐owned welfare‐maximizing public firm competes against profit‐maximizing private firms. We use a circular city model with quantity‐setting competition. In contrast to a pure market case discussed by Pal (1998a) , spatial agglomeration of private firms always appears in equilibrium. All private firms locate at the same point, and the public firm locates at the opposite side. We also find that this equilibrium pattern of the location is second best provided that output of each private firm cannot be controlled by the social planner. JEL Classification: H42, L13 Oligopole mixte et agglomération spatiale Les auteurs examinent un marché mixte où une entreprise publique possédée par l’État et cherchant à maximiser le niveau de bien‐être est en concurrence avec des entreprises privées qui cherchent à maximiser leurs profits. On utilise un modèle de cité circulaire où la concurrence se fait en choisissant la quantité produite. En contraste avec le cas du marché parfait discuté par Pal (1998a), l’agglomération spatiale des entreprises privées paraît être en équilibre. Toutes les entreprises privées se localisent au même point, et l’entreprise publique se localise du côté opposé. Il appert que ce pattern d’équilibre de localisation est un équilibre de second ordre compte tenu du fait que la production de chaque entreprise privée ne peut être contrôlée par le planificateur social.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.000 |
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.
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".