Bibliometric Analysis of Robotic Process Automation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Robotic process automation (RPA) is a recent technology that focuses on automating routine, repetitive, rule-based human operations with the intention of giving firms that choose to utilize such software a competitive edge. The scholarly literature on robotic process automation is still lacking since it is a relatively new technology that has just entered the market. So, the goal of this article is to find out how academics define robotic process automation and how much research has been done on it in terms of state, number of papers, authors citations, keyword networks, country-by-country number of publications and type of source, GS score of paper, citations per year author by author, citations per year title by title, trends, and robotic process automation applications. In the study, Bibliometric Analysis based on Scopus and WoS databases has been conducted for 22 years, from 2000 to the year 2022. The paper provides the results of the undertaken Bibliometric Analysis on Robotic process automation. It was explored in the Research that Research on robotic process automation across the globe is being populated. And India is prominently digging deep into robotic process automation research and securing the second position and, most importantly, surpassing China in the domain, despite being a less techno-savvy nation. For Bibliometric Analysis and developing network, vosviwer and publish or perish software was used by the researcher, respectively.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.033 | 0.028 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it