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
In large-scale regression problems where the dimension of the predictors p and number of observations n are large, subsampling is sometimes used to approximate least squares estimates. One approach to this is algorithmic leveraging, which draws a subsample of size m ≪ n from the observations where high leverage observations (according to the diagonals of the hat matrix) are sampled with higher probability; we can then estimate the regression parameter using either ordinary (unweighted) or weighted least squares using the sampled observations. In this paper, we will consider the properties of estimates based on subsampling by expressing these estimates as weighted averages of elemental estimates. In the case of algorithmic leveraging, this approach provides some theoretical justification to the empirical evidence that unweighted estimation outperforms weighted estimation in high leverage designs.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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