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 notion of boosting originated in the Machine Learning literature in the 1980's [VALIANT, L.G. (1984). A theory of the learnable. In Proc. 16th Annual ACM Symposium on Theory of Computing 436-445. ACM Press, New York]. The goal of boosting is to improve the generalization performance of weak (or base) learning algorithms by combining them in a certain way. The first algorithm of this type was discovered by Schapire [SCHAPIRE, R.E. (1990). The strength of weak learnability. Machine Learning 5 197-227] and then the second one by Freund [FREUND, Y. (1995). Boosting a weak learning algorithm by majority. Inform. and Comput. 121 256-285]. Schapire and Freund [FREUND, Y. and Schapire. R.E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. System. Sci. 55 119-139] came up with the idea of a more practical version of boosting and invented the algorithm called AdaBoost that combines simple classification rules into much more powerful and precise classification algorithms. For a fixed number of iterations, AdaBoost runs the weak (or base) learning algorithm on resampled original data sets in a sequential manner and then combines the resulting learning algorithms through a weighted summation at the end of the iteration. Gradually, it became clear that AdaBoost is a special case of a more general statistical methodology of combining simple estimates in classification or regression into more complex and more precise ones. The study of statistical properties of these methods has been conducted in several directions since then in both the machine learning and statistics communities. The problem of consistency of AdaBoost is posed by Leo Breiman in the first paper in this issue of The Annals of Statistics. Breiman studies one ingredient needed to prove the consistency, the convergence properties of AdaBoost as a numerical method in the population case. This paper has been circulated for a couple of years as a preprint and its results were also covered in the Wald Lectures delivered by Breiman at the IMS Annual Meeting in 2002 in Banff, Canada. The papers by Jiang, Lugosi and Vayatis, and Zhang, published below with discussions, consider various versions of boosting and give answers to the consistency question posed by Breiman.
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 | 0.000 | 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 | 0.000 |
| 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