Formulating measures for structured document retrieval search tasks using extended structural relevance
Bibliographic record
Abstract
Structured document retrieval (SDR) systems minimize the effort users spend to locate relevant information by retrieving sub-documents (i.e., parts of, as opposed to entire, documents) to focus the user's attention on the relevant parts of a retrieved document. SDR search tasks are differentiated by the multiplicity of ways that users prefer to spend effort and gain relevant information in SDR. The sub-document retrieval paradigm has required researchers to undertake costly user studies to validate whether new IR measures, based on gain and effort, accurately capture IR performance. We propose the Extended Structural Relevance (ESR) framework as a way, akin to classical set-based measures, to formulate SDR measures that share the common basis of our proposed pillars of SDR evaluation: relevance, navigation and redundancy. Our experimental results show how ESR provides a flexible way to formulate measures, and addresses the challenge of testing measures across related search tasks by replacing costly user studies with low-cost simulation.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.003 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".