Nonparametric Estimation of Returns to Scale: Method and Application
Bibliographic record
Abstract
The paper describes a method of estimating variable returns to scale in production that adaptively fits spline functions, using model selection criteria, to determine the appropriate number and location of break points for a fixed factor of production. Unlike other approaches, the method obtains nonparametric estimates of variable returns to scale for small samples while ensuring global curvature and flexibility properties are maintained. An application of the method is presented using data from the British Columbia sablefish fishery. Les auteurs décrivent une méthode d'estimation des variations de rentabilité en fonction de l'échelle de production susceptibles de s'ajuster è des fonctions spline, utilisant des critéres modèles de sélection pour déterminer le nombre et la situation des seuils de rentabilité pour unfacteur de production fixe. À la différence d‘autres avenues d‘analyse, la méthode permet d‘obtenir des valeurs non paramétriques de la rémunération variable selon l'échelle, è partir de petits échantillons, tout en garantissant le maintien de la courbure générale et de la souplesse d'application. Les auteurs proposent une application de la methode, utilisant les données des pêches de morue charbonnière en Colombie‐Britannique.
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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 | 0.008 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".