Shanghai Rankings of Global Universities (2020) and Status of Indian Universities
Bibliographic record
Abstract
Academic ranking of world universities (ARWU) 2020 is released as usual on 15 August by Shanghai Ranking Consultancy of China. ARWU has been presenting the ranking of world's Top 500 universities annually since 2004 but in 2020, ranking of 1000 global universities has been projected. It is unfortunate that none of the Indian universities and IITs find a slot among the Top 500. Indian Institute of Science, Bangalore was placed among the Top 500 in 1919 ranking but it has slided down to lower rank in the 501-600 series. USA has maintained its position with 45 universities among Top 100, Europe has improved from 35 to 36, Asia-Oceania improved from 17 to 19, and Africa remains out of any reckoning as in the past. Individual ranking of European countries has improved, for example, UK (8), France (5), Switzerland (5), Sweden (3) have improved their ranking position, while Canada( 4), Germany (4), China (6), Japan (3) and Russia( 1) maintain the status quo. In subject-wise ranking, Punjab University, Chandigarh maintains its slot among top 201-300 in the subject of Physics. In Mechanical Engineering, Indian Institutions have the better ranking among Top 400 as compared with other subjects but in other branches they hardly find a slot among Top 500. The ranking of Indian Universities is dismal in Agriculture, Biological Sciences, Social Sciences and Education.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.008 |
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".