Bibliographic record
Abstract
This thesis investigates the potential of using search agents to analyze the performance of interactive information retrieval systems. An evaluation framework uses idealized and simulated individual differences in hypertext search strategies to simulate differences in human search performance and link selection behaviour. Five distinct search strategies were identified and implemented in software. The software simulator allowed the search agents to interact with an information retrieval system, which employed dynamic hypertext as its interactive search mechanism. Three studies investigated the performance and behaviour of the search agents. The first study compared three embedded hypertext link-ranking strategies (which used textual similarity assessment algorithms) with corresponding human link rankings. Although the three algorithms studied had low overall correlation with the human participants, the analysis revealed that the algorithms performed better (relative to human judgments) under specific conditions (e.g., short vs. long documents) and were more highly correlated with the participants under those conditions. The second study compared performance when using different combinations of settings for the parameters that were relevant to each agent. Based on the results of this study, the best performing internal parameters for each agent were chosen and then used in a third study. The third study compared the performance of the agents with two different experimental factors, one representing variations in query tail size (i.e., how much of the prior search history/link selections were reflected in the current automatically generated query), and in newness (i.e., the extent to which agents were permitted to return to previously viewed documents). Both query tail size and newness affected the performance of the agents (precision decreased when newness increased and precision increased when query tail increased). This dissertation describes a framework for evaluating the effect of different search strategies and a search agent simulator based on the framework. The results of the simulation studies demonstrated a difference in terms of performance and link selection behaviour between the search agents. Effects of query difficulty and novelty on agent performance/behaviour were also identified. The simulator developed in this research should provide a useful platform for future studies of idealized search behaviour in interactive information retrieval.
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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 | 0.006 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".