ASSSESING UNIVERSITYS’ POPULARITIES OF UNİVERSITY RANKINGS BY USING GOOGLE TRENDS
Bibliographic record
Abstract
Purpose: In this research, in the world rankings; It is aimed to reveal the important criteria, to compare the search volume and to investigate the popularity of the rankings using Google Trends data. Method: In the study, Google Trends data was used for the popularity and awareness of world rankings. While obtaining data from Google Trends, the search interest of the world rankings was examined by selecting the categories "worldwide", "last 12 months", "all categories" and "google web search". In addition, the ranking criteria published in 2022 on the web pages of URAP, ARWU, CWUR, CWTS-Leiden, NTU, QS, RUR, US News, SCImago-SJR, THE and Webometrics were examined.Finding: In this study, the world rankings were evaluated on criteria such as data and indicators made on Google Trends. THE is at the forefront of popularity, with QS 69 regions in over 70 regions worldwide. US News follows this with 31 regions, Webometrics with 15 regions, and ARWU with 12 regions, demonstrating its recognition. URAP 11 regions, SJR ten regions, CWUR eight regions, Leiden University seven regions, NTU six regions and finally RUR three regions were searched worldwide. In addition, it is seen in the findings obtained as a result of Google Trends search volume; the regions using different world rankings are the USA and India; We frequently use THE, QS, US News, Webometrics, ARWU rankings, Canada's THE, QS, US News, ARWU rankings, Germany, China and Turkey's THE, QS, US News rankings, and Russia's THE and QS rankings. appears to be in use and popular.Conclusion: As a result of the research; It is seen that some of the leading rankings such as THE, QS, US News, Webometrics, ARWU, URAP, SJR, CWUR, CWTS-Leiden, NTU and RUR are strongly based on bibliometric data and have high similarities, from the most popular and well-known, respectively. Research, teaching and citations are emphasized in the world rankings criteria; It also includes comprehensive measures of international outlook, reputation survey, citations, academic excellence, and industrial collaboration. It has also been concluded that popular world rankings are used more in densely populated countries. As a result, the study, which was built in line with the purpose of the research, is seen as an important resource for evaluating the excellence of universities across global university rankings, and it is of importance for every segment existing and affected at the education level conclusion is reached. Because both state and private universities can be considered as political institutions of the state.Inference: There should be an increase in popularity and awareness of Google Trends in order to fulfill the basis of the ranking criteria. For this reason, higher education institutions want to take part in the ranking race to use the talent attracting capacity of the nations.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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 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".