UNDERSTANDING THE EVOLUTION OF INFORMATION SYSTEMS RESEARCH FROM THE PERSPECTIVE OF CO-AUTHORSHIP NETWORK: A COMPREHENSIVE DATA ANALYSIS FROM 1993 TO 2012
Bibliographic record
Abstract
Based on the articles published in three top journals in the field of information systems (MISQ, ISR and JMIS) from 1993 to 2012, we conduct a research of the structure, characteristics and development trend of co-authorship network through scientometrics and social network analysis approaches. We gain a number of insights after synthetical analysis. In the last two decades the whole co-authorship network density in information systems faces a tendency of decrease. The co-authorship network presents properties of “small world”. The number of articles published by scholars and institutions in the three elite journals all display a ‘long tail’ phenomenon. The field of information systems has a stable development in the biggest component, and has not yet went into a mature and steady stage. Quite a lot of outstanding scholars and educational resources came from USA, Canada and Hong Kong, and USA has held eight institutions of the top ten. The ranking of an entire institution can be influenced by even one or two authors, indicating that outcome from one level might propagate to the next level.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.018 | 0.026 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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, 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".