Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Citation (2009), "Nonparametric Econometric Methods", Li, Q. and Racine, J.S. (Ed.) Nonparametric Econometric Methods (Advances in Econometrics, Vol. 25), Emerald Group Publishing Limited, Bingley, p. iii. https://doi.org/10.1108/S0731-9053(2009)0000025022 Publisher: Emerald Group Publishing Limited Copyright © 2009, Emerald Group Publishing Limited Book Chapters Advances in econometrics Nonparametric Econometric Methods Copyright page List of contributors Call for Papers Introduction Partial identification of the distribution of treatment effects and its confidence sets Cross-validated bandwidths and significance testing Semiparametric estimation of fixed-effects panel data varying coefficient models Functional coefficient estimation with both categorical and continuous data The evolution of the conditional joint distribution of life expectancy and per capita income growth A nonparametric quantile analysis of growth and governance Nonparametric estimation of production risk and risk preference functions Exponential series estimation of empirical copulas with application to financial returns Nonparametric estimation of multivariate CDF with categorical and continuous data Higher order bias reduction of kernel density and density derivative estimation at boundary points Nonparametric and semiparametric methods in R Some recent developments in nonparametric finance Imposing economic constraints in nonparametric regression: survey, implementation, and extension Functional form of the environmental Kuznets curve Some recent developments on nonparametric econometrics
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.022 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.007 |
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 it