Bibliographic record
Abstract
Introduction Web queries tend to be significantly shorter and less complex than queries used in earlier types of information systems (Jansen & Pooch, 2000; Lawrence & Giles, 1999; Spink et al, 2001). Yet, there is general belief that enriched queries and query reformulation will lead to improved results (Belkin et al, 2001). In our research we are examining the sorts of tools that could assist with the creation of enriched queries and in turn improve the search process and the user's search experience. In the work reported here we assessed the use of two types of tools: one to assist the user in targeting and, thus, restricting the query, and a second one to assist in augmenting the query. We speculated that certain types of tools are more useful for certain types of information tasks. In particular we targeted the standard informational request in which a suitable response could be culled from many different Web pages, and secondly, the 'know-item' task, in which a specific Website
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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 source (direct Gemma or distilled Codex), 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".