Mapping the research trend and international collaboration of IIT Delhi
Bibliographic record
Abstract
Purpose: The primary purpose of this study is to find out the trend in research publications and the growth of international collaboration in the domain of science, engineering, technology, management, etc., of IIT Delhi from 2001 to 2022 within India and globally. Methodology: Web of Science is one of the largest and most reputed bibliographic databases covering global publications in science, engineering, technology, etc. The publication records covered in the Web of Science database were extracted using the affiliation search for 22 years from 2001 to 2022, and the publications found are 24893. The scientometric techniques have been used to identify research trends, international collaborations, and research impact. Findings: Almost all publications are in English, except two papers, and most are article types. The number of publications has consistently grown. The number of citations received also increased over time. The collaboration in research publications has consistently grown over the last 22 years. The difference in the average citations between with and without international collaborated publications is 5.14. The data shows that the USA is the leading country in research publications, but Canada has the highest average number of citations per publication. Research limitations: The study is confined to the Indian Institute of Technology Delhi, and the period is restricted from 2001 to 2022. Originality: A few studies have been found on institutions’ global collaboration and impact on the scientific community. This study benefits the researchers, educationists, administration, and sponsoring bodies to make informed decisions and investments.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.019 | 0.056 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".