Testing for the Eigenvector Based on the Multiple Correlation Coefficient
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
We propose a novel method for testing the hypothesis of an eigenvector based on the exact distribution of the multiple correlationcoefficientunderanormalpopulation. Inparticular, wediscussbothnonsingularandsingularcases, addressing the relationship between sample size and the number of variables. The proposed test has the advantage of being invariant to the ordering of the target eigenvector, focusing only on whether the target vector is an eigenvector. The ordering of the eigenvector is determined by the minimum angle between the target vector and the sample eigenvector. Furthermore, we demonstrated that type I errors is exactly controlled at a particular significance level, and the power under the specified alternative hypothesis can be calculated by the Gauss hypergeometric function in the nonsingular case. Our simulation studies confirm that the empirical distribution of the test statistic is in agreement with theoretical 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 | 0.003 | 0.048 |
| 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) | 0.000 | 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