Bibliographic record
Abstract
We present two exact versions of the quantitative expansion property first presented in Angel et al. (2014), called the Perfect Expansion Property and the disjoint Perfect Expansion Property ( PEP and DPEP ). This gives a direct combinatorial way of establishing the unique ergodicity of automorphism groups of Fraïssé classes, without having to use the probabilistic arguments in Angel et al. (2014). We focus on the special case of , the class of complete, -partite digraphs. Not all structures in this class have the PEP and we classify which structures have the stronger DPEP . The structures with this expansion property are intimately connected with the definable geometric structure of a Fraïssé structure. We also look at the PEP for semigeneric digraphs, but we do not settle the question of unique ergodicity of the automorphism group of the semigeneric digraph. 1 Surprisingly, there are non-trivial substructures of the semigeneric digraph with the PEP .
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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 | 0.001 | 0.001 |
| 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 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".