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
This chapter describes the linear regression estimator with one auxiliary variable, initially in the design-based or fixed-population context. It covers the regression estimation with unequal probability designs and multiple regression models. Like the ratio estimator, the regression estimator is not design-unbiased under simple random sampling. Under usual regression model assumptions, however, the estimator is unbiased. If a regression model describing a stochastic relationship between the auxiliary variables and the variable of interest is assumed, a natural objective of sampling is the “prediction” of some characteristic of the y-values of the population. The characteristic to be predicted may be the population mean or total or the y-value of a single unit not yet in the sample. The basic results of the linear prediction approach are summarized for the simple linear regression model with one auxiliary variable and then in general for multiple regression models with any number of auxiliary variables. Controlled Vocabulary Terms linear regression; Multiple regression; probability distribution
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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