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
Cleaning data or removing some data periods in least squares (LS) regression analysis is not unusual. This practice indicates that a researcher sometimes desires to estimate the parameter value, with which the regression function fits a large fraction of individuals or events in the population (behind the original data set), possibly exhibiting poor fits to some atypical individuals or events. The S‐estimators are a class of estimators that are consistent with the researcher's desire in such situations. In this paper, we propose a method of model selection suitable in the S‐estimation. The proposed method chooses a model that minimizes a criterion named the penalised S‐scale criterion (PSC), which is decreasing in the sample S‐scale of fitted residuals and increasing in the number of parameters. We study the large sample behavior of the PSC in nonlinear regression with dependent, heterogeneous data, to establish sets of conditions sufficient for the PSC to consistently select the best‐fitting, most parsimonious model. Our analysis allows for partial unidentifiability, which is an important possibility when selecting one among non‐linear regression models. We conduct Monte Carlo simulations to verify that a particular PSC called the PSC‐S is at least as trustworthy as the Schwarz information criterion, often used in the LS regression.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it