Science Research Output pattern of University of Delhi (2015-2019)
Bibliographic record
Abstract
The current research has been conducted to tress out the science research output of University of Delhi (DU) in the last five years (2015-2019) after using Web of Science (WOS) database. The present study has used Web of Science databases to collect the science research output of University of Delhi for the specified period. The retrieved data were analyzed using specific parameters. This study investigates the most productive institutes, countries, authors the impact of their output in terms of Relative Citation Impact (RCI) and Citation per Paper (CPP). For visualizing purposes, VOS Viewer has been used. We retrieved 6500 papers from Web of Science, consisting of 87.6% journal articles, 6.29% proceeding papers, and 6.15% review articles. The analysis of data indicates that consistent growth with increasing multi-authorship is the general trend of research. Multiauthored papers with international collaboration have more research impact (CPP, RCI) compared to others. USA, Germany, Korea topped the list of collaborating countries in science research. However, Canada made the very best effect in phrases of CPP and RCI. The University of Delhi has a major collaboration with BHU, JNU, IIT, and CSIR in terms of domestic collaboration. The study can be better used for further identification of research areas in sciences where attention can be given.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.018 | 0.048 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".