Bibliographic record
Abstract
Foundation (SBR 98-18875, 4/99-12/02) The opinions expressed in this paper are those of the authors and do not necessarily reflect the opinions of the Federal Reserve Board of Governors or its staff. Very helpful comments and suggestions were pro-vided by JR DeShazo, David Layton, and session participants at the Second World Congress of Environmental and Resource Economists (Monterey, CA, June 2002). The authors thank Vilija Gulbinas for able research assistance during the program-ming of the survey, and countless faculty at colleges and universities throughout the US and Canada who have introduced the online survey to their classes and encouraged them to participate. Without their generous help, this study could not have been completed. 1 Eliciting Individual-Specific Discount Rates Longstanding debate over the appropriate social discount rate for public projects stems from our lack of knowledge about how individual discount rates vary across people and across choice con-texts. Using a sample of roughly 15,000 choices by over 2000 individuals, we estimate utility-theoretic models concerning private tradeoffs involving money over time that reveal individual-specific discount rates. We control for experimentally differentiated choice scenarios, sociodemo-graphic heterogeneity, and elicitation formats, and complex forms of heteroscedasticity. Statisti-
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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 | 0.008 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".