A Decomposed Negative Binomial Model of Structural Change: A Theoretical and Empirical Application to U.S. Agriculture
Bibliographic record
Abstract
We developed a single‐equation decomposed negative binomial regression model (NBRM) of the U.S. farm sector to simultaneously evaluate structural changes in the U.S. agricultural sector and the strength of several economic forces that influenced the changes in farm structure during the 1960–96 period. We found all these forces reinforced economic incentives to increase the size and decrease the number of small farms. Only agricultural programs and machinery prices countered these forces. Nous avons élaboré un modèle de régression binomiale négative (NBRM) décomposéàéquation unique pour le secteur agricole des États‐Unis afin d'évaluer simultanément les changements structurels de ce secteur ainsi que la puissance de plusieurs forces économiques qui ont influencé les changements de structure des exploitations agricoles au cours de la période 1960–96. Nous avons conclu que toutes ces forces ont renforcé les stimulants économiques en faveur d'une augmentation de la taille des exploitations et d'une diminution du nombre de petites exploitations. Seuls les programmes agricoles et le prix de la machinerie ont contrecarré ces forces.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".