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
We propose a simple and flexible framework for offline evaluation based on a weak ordering of results (which we call "partial preferences") that define a set of ideal rankings for a query. These partial preferences can be derived from from side-by-side preference judgments, from graded judgments, from a combination of the two, or through other methods. We then measure the performance of a ranker by computing the maximum similarity between the actual ranking it generates for the query and elements of this ideal result set. We call this measure the "compatibility" of the actual ranking with the ideal result set. We demonstrate that compatibility can replace and extend current offline evaluation measures that depend on fixed relevance grades that must be mapped to gain values, such as NDCG. We examine a specific instance of compatibility based on rank biased overlap (RBO). We experimentally validate compatibility over multiple collections with different types of partial preferences, including very fine-grained preferences and partial preferences focused on the top ranks. As well as providing additional insights and flexibility, compatibility avoids shortcomings of both full preference judgments and traditional graded judgments.
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 | 0.001 |
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