Bibliographic record
Abstract
As farms increase in size, operators face the decision of remaining loyal to local retail merchants or obtaining volume discounts from distant wholesale input suppliers. When farmers bypass local merchants and buy inputs in volume, they often realize price discounts but forego many services including credit forbearance. When farmers buy locally, they pay higher prices, which decreases profits and increases financial risk, but generates social capital which can be drawn upon during periods of economic adversity. A theoretical model of farm financial risk evaluates borrower behavior in light of cash flow constraints, volume discounts, and social capital. Results delineate financial risks involved and value of social capital. When inputs are purchased locally and social capital is generated, the distribution of year‐end funds had a slightly lower mean and longer left tail. The longer left tail results from additional borrowing arising from credit forbearance. If this forbearance were not available, the firm would be bankrupt. À mesure que la taille des exploitations agricoles augmente, les producteurs doivent décider s'ils demeurent loyaux envers les détaillants locaux ou s'ils tentent d'obtenir des remises sur quantité auprès de fournisseurs d'intrants éloignés. Lorsque les producteurs contournent les marchands locaux et achètent des intrants en grande quantité, ils obtiennent souvent des rabais sur le prix, mais se privent de nombreux services y compris l'indulgence des créanciers. Lorsque les producteurs achètent localement, ils paient des prix plus élevés, ce qui diminue les profits et augmente le risque financier, mais crée du capital social qui peut être mis à contribution en périodes d'adversitééconomique. Un modèle théorique de risque financier agricole évalue le comportement de l'emprunteur en tenant compte des contraintes de liquidités, des remises sur quantité et du capital social. Les résultats présentent en détail les risques financiers en jeu et la valeur du capital social. Lorsque les intrants sont achetés localement et qu'il y a création de capital social, la queue gauche de la distribution des fonds de fin d'année est plus longue et la moyenne est légèrement plus faible. Cette queue gauche plus longue découle des emprunts supplémentaires résultant de l'indulgence des créanciers. Sans cette indulgence, l'entreprise serait en faillite.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".