The scaling relationship between degree centrality of countries and their citation-based performance on Management Information Systems
Bibliographic record
Abstract
The aim of this paper is to explore the power-law relationship between the degree centrality of countries and their citation-based performance in Management Information Systems research. We analyzed 27,662 articles that received 127,974 citations. The distribution of the citation-based performance follows a power law with exponent of −2.46 ± 0.05. The distribution of the centrality degree of countries follows a power law with exponent of −2.26 ± 0.24. The citation-based performance and degree centrality exhibited a power-law correlation with a scaling exponent of 1.22 ± 0.04. Citations to the articles of a country in MIS tend to increase 21.22 or 2.33 times each time it doubles its degree centrality in the international collaborative network. Policies that encourage a country to increase its degree centrality in a collaboration network can disproportionately increase the impact of its research.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".