Re-ranking search results using network analysis a case study with google: a case study with Google
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
In this paper we review methods of structured search for information on the World Wide Web. We propose new methods based on co-citation and network analysis. We describe a set of 21 measures based on these methods and examine the factor structure of those measures. We then report on a recent study that we have conducted at the University of Toronto. Human judges rated the relevance of a selection of Web pages returned by the Google search engine for each of seven queries. We compared the average judged relevance of the top 20 search results selected by Google vs. the top 20 results as selected by each of the 21 network analysis measures. All but one of the network analysis measures (inlink) showed significantly (p<.05) better (as compared to Google) average judged relevance amongst their top 20 selections. Stepwise regression analysis was then used to identify a linear model with three network analysis measures as predictors, which accounted for roughly 17% of the variance in relevance judgments. While these results need to be extended with more detailed analysis of a wide range of queries and topics, they suggest that network analysis of search output adjacency matrices (where adjacency/proximity is based on web-wide co-citations) may significantly improve search engine rankings.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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