The Use of Literature Based Elasticity Estimates in Calibrated Models of Trade-Wage Decompositions: A Calibmetric Approach
Bibliographic record
Abstract
How to best utilize the wide range of estimates of elasticities that characterize econometric literature when using calibrated models is the issue we address here through a blending of econometrics and calibration into calibmetrics.Econometrically generated literature based elasticity parameters are typically used in calibrated models a very simple manner, appealing to a single value.Here we explicitly incorporate the full range of values of elasticities yielded by econometric studies in both the calibration procedure employed and the uses made of a calibrated model.This is important because the ranges for such values can be large.This allows us to assess how uncertainty in exogenously specified parameter values affects the performance of calibrated models, and how much added information is obtained by using the full range of literature estimates of key parameters in calibration.
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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 | 0.033 | 0.186 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".