Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The aim of this article is to distinguish between strategies in the Iterated Prisoner’s Dilemma on the basis of their relative performance in a given population set. We first define a natural order on such strategies that disregards isolated disturbances, by using the limit of time-average payoffs. This order allows us to consider one strategy as strictly better than another in some population of strategies. We then determine a strategy σ to be ‘‘robust,’’ if in any population consisting of copies of two types of strategies, σ itself and some other strategy τ, the strategy σ is never worse than τ. We present a large class of such robust strategies. Strikingly, robustness can accommodate an arbitrary level of generosity, conditional on the strength of subsequent retaliation; and it does not require symmetric retaliation. Taken together, these findings allow us to design strategies that significantly lessen the problem of noise, without forsaking performance. Finally, we show that no strategy exhibits robustness in all population sets of three or more strategy types.
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.
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 | 0.000 |
| 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 | 0.001 |
| 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