Bibliographic record
Abstract
It is proposed that statistics for comparing dispersion (i.e., spread, scale; e.g., variance) when data are tied should be tie-centered. For statistics which are score sums, this means that if the highest score (indicating the least difference from a center of the data) is assigned to the. median when there are no ties, then the median should have the highest score when there are ties. Using standard methods for ties, most popular dispersion rank test statistics are not tie-centered, e.g., the Ansari- Bradley, Mood and Siegel-Tukey statistics. A new tie-centered rank test statistic with a Wilcoxon distribution is proposed for ordered categorical data. For data consisting of meaningful point values with many ties, the Fligner- Killeen test is proposed. Exact tests and random cut-points tests can be done with these statistics, and cluster sampling can be accommodated.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.068 | 0.423 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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 source (direct Gemma or distilled Codex), 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".