Bibliometric analysis of global scientific research on Public Administration: 1923-2020
Bibliographic record
Abstract
This study aims to perform a bibliometric analysis of documents published in the field of Public Administration during the years 1923-2020. In this bibliometric study, all Web of Science (WOS) databases were used to retrieve the publications in this field. Using a proper search strategy, 93093 records were retrieved in the WOS database from 1923 to 2020. Excel and VOSviewer software were used for bibliometric analysis and visualization of documents. The findings show that 64.31% of documents (59860 documents) were articles; most documents were published in the Public Administration Review-Journal (n= 9011). The United States (with 31930 documents), ENGLAND (with 14636 documents), and Canada (with 7104 documents) published the most documents in this field, respectively. The University of Birmingham was the most productive institution (n=1,441, 1.54 %). Meier, K. J. S was the most productive author (n= 119, 0.12%). Keywords with the highest frequency were "management", "governance", "government", "policy", "performance", "politics", "state", and "organizations". The most co-occurrence keywords existed within three clusters, the first including keywords related to policy issues, the second including author keywords about management and performance, and the third including keywords related to state and local management. The global trend of publications in the field of Public Administration has been upward, from 54 documents in 1923 to 4561 documents in 2020. This study not only presents a full view of global Public Administration research but also can contribute to future research in this field and bibliometric studies.
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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 | 0.009 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.120 | 0.210 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".