Grid Computing Research from India: A Bibliometric Assessment of Publications during 2008–2017
Bibliographic record
Abstract
The paper examines quality and quantity of scientific research in India as reflected in publications output on grid computing on a series of bibliometric indicators. Data were extracted from Scopus database consisting of 1340 publications in grid computing research from India in 10 years during 2008–17 citation impact of 4.33 citations per paper and international collaborative publication share of 12.16%. The paper profiles global publication output and share of 10 most productive countries in grid computing research, 15 most productive Indian organizations and 15 most productive authors on a series of indicators including publications output, number of citations, the relative citation index, citations per paper, h-index and share of international collaborative papers during 2008–17. Computer science, among top 3 subjects, accounted for the largest publication share (90.15%), followed by engineering (26.79%) and mathematics (13.66%). The 15 most leading organizations and authors together contributed 31.57% and 14.25% as their share of Indian publication output and 44.93% and 16.17% as their share of Indian citation output respectively during 2008–17. The most productive Indian organizations were Anna University, Chennai (74 papers) and Thapar University, Patiala (48 papers) The most productive authors were I. Chana of Thapar University, Patiala (21 papers) and N. Mukherjee of Jadavpur University, Kolkata (18 papers). The 15 most productive journals contributed 43.92% share to the Indian journal publication output during 2008–17. The most productive journals were Applied Engineering Research (16 publications), Journal of Grid Computing (14 publications) and Future Generation Computer Systems (13 publications),
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.021 | 0.011 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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 itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".