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
Abstract With recent advances in the power of personal computers, reference distributions of statistics are now often generated using computer‐intensive methods. Several terms are erroneously used interchangeably when referring to such computer‐intensive statistical methods, including ‘resampling techniques’, ‘Monte Carlo’, ‘permutation’, ‘randomization’ and ‘bootstrap’. These techniques are not interchangeable but are fundamentally different in terms of their statistical mechanics. The generic terms ‘resampling techniques’ and ‘computer‐intensive methods’ refer to all methods in which the observed data are used to generate a reference distribution by means of randomization. This reference distribution is then used to assess the significance of a statistic calculated from the observed (not randomized) data. Significance is evaluated under the assumption that the statistic computed using the observed data is sampled from the reference distribution generated with a randomization technique.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.029 | 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 it