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 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".