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
In a series of papers starting in the late 1980s, D. A. S. Fraser, N. Reid and coworkers developed the tangent exponential model for higher-order likelihood inference. This chapter aims to explain the motivation and justification for this model and to describe how it is used to compute accurate approximations. The literature on this is not entirely transparent, as the argument evolved over numerous articles (Fraser, 1988, 1990, 1991, 2004; Fraser and Reid, 1988, 1993, 1995, 2001; Cakmak et al., 1994; Fraser et al., 1999). We give a heuristic account of this construction, for the most part skating over the technical details, and provide an annotated bibliography as a road map through the literature. The high accuracy of the resulting approximations has been verified empirically both in numerous articles and in books such as Brazzale et al. (2007), Chapter 8 of which overlaps with the account here.
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) | 0.002 | 0.001 |
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