Bibliographic record
Abstract
The appropriate interpretation of bootstrap support for splits and the question of what constitutes large bootstrap support have received considerable attention. One desirable interpretation, indeed the interpretation that was put forward when bootstrap support for splits was first introduced, is that 1-minus bootstrap support is a P value for the hypothesis that the split is not well resolved. As a P value, bootstrap support has been argued to be first-order correct. By obtaining the limiting distribution of bootstrap support for a split when maximum likelihood estimation is conducted, it is shown that bootstrap support is not first-order correct and insight is provided into the nature of the problem. Borrowing from earlier results, it is also shown that similar results hold when the neighbor-joining algorithm is used. Examples suggest that bootstrap support is generally conservative as a P value and give insight as to why this is usually the case. The analysis indicates that the problem is largely due to the unusual nature of tree space where boundary trees always have at least 2 neighbors.
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.
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 | 0.098 | 0.529 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.006 | 0.015 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".