A Discrete‐Time Duration Analysis of Technology Disadoption: The Case of rbST in California
Bibliographic record
Abstract
Technology choice is an inherently dynamic process that is best modeled as a repeated decision conditional on past decisions and the current/expected economic environment. Duration analysis recognizes this dynamic nature and focuses on the timing of economic decisions. Most duration studies assume that events occur continuously but in many cases, this is an unrealistic assumption; many events of interest occur at discrete intervals. We demonstrate the use of a discrete‐time duration model with an empirical example: the decision to use recombinant bovine Somatotropin (rbST) by California dairy producers. The results of the discrete‐time duration analysis suggest that a temporary shortage of rbST had a negligible effect on rbST use, while an rbST ban had a significant and negative effect on rbST use. Le choix d’une technologie est un processus intrinsèquement dynamique qui est le mieux modélisé en tant que décision répétée conditionnelle aux décisions antérieures et à l’environnement économique actuel ou prévu. L’analyse de durée reconnaît cette nature dynamique et est principalement axée sur le choix du moment des décisions économiques. La plupart des analyses de durée supposent que les événements surviennent de façon continue mais dans de nombreux cas, il s’agit d’une hypothèse irréaliste; bon nombre d’événements d’intérêt surviennent à intervalles discrets. Dans la présente étude, nous avons fait la démonstration d’un modèle de durée à temps discret à l’aide d’un exemple empirique: la décision d’utiliser ou non la somatotropine bovine recombinante (STbr) chez les producteurs de lait en Californie. Les résultats de notre analyse de durée à temps discret montrent qu’une pénurie temporaire de STbr a eu un effet négligeable sur l’utilisation de cette hormone, tandis qu’une interdiction a eu un effet négatif considérable.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".