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
Context such as the user’s search history, demographics, devices, and surroundings, has become prevalent in various domains of information seeking and retrieval such as mobile search, task-based search, and social search. While evaluation is central and has a long history in information retrieval, it faces the big challenge of designing an appropriate methodology that embeds the context into evaluation settings. In this article, we present a unified summary of a wide range of main and recent progress in contextual information retrieval evaluation that leverages diverse context dimensions and uses different principles, methodologies, and levels of measurements. More specifically, this survey article aims to fill two main gaps in the literature: First, it provides a critical summary and comparison of existing contextual information retrieval evaluation methodologies and metrics according to a simple stratification model; second, it points out the impact of context dynamicity and data privacy on the evaluation design. Finally, we recommend promising research directions for future investigations.
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 | 0.029 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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