Bibliographic record
Abstract
Classical conjectures due to Littlewood, Erdős and Golay concern the asymptotic growth of the $L_p$ norm of a Littlewood polynomial (having all coefficients in $\{1, -1\}$) as its degree increases, for various values of $p$. Attempts over more than fifty years to settle these conjectures have identified certain classes of the Littlewood polynomials as particularly important: skew-symmetric polynomials, reciprocal polynomials, and negative reciprocal polynomials. Using only elementary methods, we find an exact formula for the mean and variance of the $L_4$ norm of polynomials in each of these classes, and in the class of all Littlewood polynomials. A consequence is that, for each of the four classes, the normalized $L_4$ norm of a polynomial drawn uniformly at random from the class converges in probability to a constant as the degree increases.
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How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".