Bibliographic record
Abstract
In this paper we attempt to (i) extend the competitive equilibrium neoclassical growth model to incorporate consumer preferences that feature temptation and selfcontrol as in the framework developed by Gul and Pesendorfer; (ii) use the model to analyze taxation and welfare; and (iii) extend and specialize the Gul-Pesendorfer temptation formulation to be dynamic and, in particular, quasi-geometric, thus providing a link to, and possibly an interpretation of, the Laibson model. # We thank Wolfgang Pesendorfer for important suggestions and help throughout this project. Krusell and Kuruscu are at the University of Rochester; Smith is at Carnegie Mellon University. We thank seminar participants at Arizona State University, Carnegie Mellon University, Duke University, Harvard University, Johns Hopkins University, New York University, Princeton University, Yale University, and the 2001 North American Summer Meetings of the Econometric Society for helpful comments. Krusell and Smith thank the National Science Foundation for financial support. 1
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How this classification was reachedexpand
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 | 0.010 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".