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
Recently developed retrieval effectiveness measures have incorporated models of user behavior, but have limited themselves to predicting user performance over a single query and response. Accurate prediction of user performance with search systems must incorporate a means to model how users switch between different information sources. For example, a search session may consist of multiple queries with the user making decisions of when to switch from evaluating the current result list to a new result list produced by a query reformulation. Likewise, users may switch to a result list produced by a query suggestion or other interaction mechanism that produces a new search result list. In this paper, we simulate user behavior and investigate optimal switching behavior for a user who must decide when and if to issue their current query to another search engine. As a first step in understanding the problem space, we restrict our investigation and discussion to two top performing runs submitted to the TREC 2005 Robust track. We find four classes of switching behavior that a user would be faced with in making a decision about whether to switch from one result list to another.
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 | 0.001 |
| 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