Web search volume as a predictor of academic fame: An exploration of <scp>G</scp>oogle trends
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
Searches conducted on web search engines reflect the interests of users and society. G oogle Trends, which provides information about the queries searched by users of the G oogle web search engine, is a rich data source from which a wealth of information can be mined. We investigated the possibility of using web search volume data from G oogle Trends to predict academic fame. As queries are language‐dependent, we studied universities from two countries with different languages, the U nited S tates and S pain. We found a significant correlation between the search volume of a university name and the university's academic reputation or fame. We also examined the effect of some G oogle Trends features, namely, limiting the search to a specific country or topic category on the search volume data. Finally, we examined the effect of university sizes on the correlations found to gain a deeper understanding of the nature of the relationships.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.019 |
| 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 it