Research Cooperation Network Analysis in the Public Administration Domain
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
We construct scientific research cooperation networks in the field of public management to provide empirical support for exploring the trend in cooperation in the public administration domain. Based on the SSCI database, the co-authored papers in the field of public administration from 1921 to 2022 are selected as data sources. Ucient software is used to visualize the cooperation networks of countries, cities, institutions, and authors in public administration research, and to explore the spatial structure and driving factors of cooperation networks at different levels. The country-level cooperation in public administration research is closely related to geographical location and is affected by regional agreements to some extent. London and Washington are located at the center of the global public administration cooperation network, and the city-level cooperation network is affected by south–north differentiation and the east–west gap in global economic development and thus exhibits significant non-equilibrium. The institutions in the United Kingdom, America, and Canada are the main forces of international cooperation in the field of public administration and accordingly occupy a dominant position in cooperation networks. The authors’ collaboration network in the public administration research shows strong centrality.
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.
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 | 0.014 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.018 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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