An Investigative Analysis on Finding Patterns in Co-Author and Co-Institution Networks for LIDAR Research
Bibliographic record
Abstract
Social Network Analysis (SNA) has proven itself to embody the complex relationships between actors of groups inside out. Not only that, but it has also emerged as a new paradigm to investigate the structure of ties and its role on relationships between the actors. This research aims to investigate the patterns of relationships between authors and institutions working in LIght Detection And Ranging (LIDAR) research area. LIDAR has been in the limelight during recent years, especially autonomous vehicles for map-making and objection detection tasks. Researchers need insight into the current contributors and research areas to devise policies and set future targets for this important technology. Current study performs SNA to identify potential institutions and researchers that can help to achieve those goals. National and international co-authorship is analysed separately. A total of 4274 papers from Web of Science (WOS) database are collected from 1998 to September 2017. SNA measures of degree, closeness, betweenness, and eigenvector centrality along with descriptive analysis are employed to study the patterns. Analysis reveals that the United States of America (USA) is the most central and significant country in terms of international co-authorship. China, Germany, the United Kingdom (UK) and Canada are ranked 2nd, 3rd, 4th and 5th in this list respectively. For co-institution network, National Aeronautics and Space Administration (NASA), University of Idaho and California Institute of Technology USA occupy 1st, 2nd, and 5th position respectively when top 5 institutions are considered. Consiglio NazionaleDelle Ricerche of Italy occupies 3rd position while Chinese Academy of Science, China, secures 4th place concerning betweenness centrality. Descriptive analysis reveals that during the last decade, co-author collaboration in scientific research has been elevated. Results show that research articles with 6 or more authors have higher citations than those with two to five authors. In addition, journals producing a higher number of papers and their corresponding citations are also discussed
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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 | 0.010 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.023 | 0.032 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".