Enriching a predicate and tame expansions of the integers
Why this work is in the frame
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Bibliographic record
Abstract
Given a structure [Formula: see text] and a stably embedded [Formula: see text]-definable set Q, we prove tameness preservation results when enriching the induced structure on Q by some further structure [Formula: see text]. In particular, we show that if [Formula: see text] and [Formula: see text] are stable (respectively, superstable, [Formula: see text]-stable), then so is the theory [Formula: see text] of the enrichment of [Formula: see text] by [Formula: see text]. Assuming simplicity of T, elimination of hyperimaginaries and a further condition on Q related to the behavior of algebraic closure, we also show that simplicity and NSOP 1 pass from [Formula: see text] to [Formula: see text]. We then prove several applications for tame expansions of weakly minimal structures and, in particular, the group of integers. For example, we construct the first known examples of strictly stable expansions of [Formula: see text]. More generally, we show that any stable (respectively, superstable, simple, NIP, NTP 2 , NSOP 1 ) countable graph can be defined in a stable (respectively, superstable, simple, NIP, NTP 2 , NSOP 1 ) expansion of [Formula: see text] by some unary predicate [Formula: see text].
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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 | 0.004 |
| 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