Tudományos láthatóság vizsgálata a rendészettudomány esetében
Bibliographic record
Abstract
The paper investigates international scientific competitiveness in institutions having a primary research profile in police sciences. This ever-increasing competition between institutions fits into the processes of a change of scientific approach at the level of individual researchers, institutions, and countries. Its dominating process is the “marketization” of science, as it becomes the source of earning of researchers. Another important process is the increasing role and significance of the international university rankings as an effective tool of performance evaluation of institutions. Six institutions are involved in the empirical analysis, including the Central Police University, College of Policing, Israel Police, National Research Institute of Police Science, Royal Canadian Mounted Police, Shandong Police College. Findings show that 79 percent of the total publications are published in journals, followed by conference proceedings and review articles. Social science research articles are in the 6th place with a total of 266 articles, while medicine is the leading discipline with 1,088 publications. By measuring the publication performance, the most popular topics can be identified as well. This list is dominated by the topic titled „Homicide; Serial Killer; Murderers”. Analyzing the co-author network, a very active and intensive network can be drawn between the institutions. Joining this network should be a hot priority for the Hungarian institutions as well, primarily for the Faculty of Law Enforcement of the University of Public Service.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.007 | 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; both teacher heads agree on what is shown here.
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".