Collaboration trend in Indian Business-Management research: A bibliometric perspective
Bibliographic record
Abstract
The study has brought out the current landscape of Indian collaboration in research in the discipline of Business management. Co-authorship patterns derived from 1997-2012 data in the EBSCO-Host BSP (Business Source Premier)dtabase are analyzed to show the status of Business Management (BM) collaboration in India. The growth of co-authored publications during the study period is calculated as 37.94 percent and single authored papers as 42.7 percent using the log- linear model. Three different levels of collaboration as local(LL) national(NL) and international(IL) are discussed. On examining the sectoral collaboration measured as papers authored by academic and non-academic authors it is found that academic contribution dominates in BM research. Indian researchers in management are more often collaborated nationally and locally than with foreign partners outside the country. International co-publication representing 30.3% of the co-authored publication shows a gradual decline over the period. On examining the affiliation of authors and co-authors it is evident that the lion share of publications are brought out by the academic collaboration. The collaboration between academic and non academic researchers is reported at very low level but shows more internationally co-authored papers. The United States dominates in partnering with India by producing more number of papers, followed by United Kingdom, China, Canada and Australia. The ranking of institutions show that the institutions like IIMs, IITs are dominating in productivity and the private sector institutes are still lagging behind.
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
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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.063 | 0.155 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".