Identification of Economies of Scope in a Stochastic Production Environment
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.
Bibliographic record
Abstract
This paper extends the definition of economies of scope to multioutput firms that face an uncertain production environment. Identification of economies of scope in this environment, however, requires separability assumptions on the technology. These identification restrictions are demonstrated in the paper. For each identification restriction, the definition of economies of scope is generalized to the case of uncertain production and risk aversion. L'article que voici élargit la définition des économies de gamme aux entreprises à produits multiples aux prises avec une situation incertaine au niveau de la production. Pour cerner les économies de gamme dans une telle situation, il faut poser l'hypothèse de la séparation des technologies. Les auteurs illustrent ces restrictions et généralisent la définition des économies de diversification pour chacune d'elles dans le cas d'une production incertaine et de l'aversion du risque.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it