Theoretic Trends in Private-Public Partnership Research: Bibliometric Analysis
Bibliographic record
Abstract
Abstract This paper conducted detailed research on publication activity in the public-private partnership (PPP) field based on bibliometric analysis. The main aim was to research the different literature materials and systematize trends and priority fields of PPP research. Scopus tools were used to determine the dynamics of the number of indexed publications, research belonging to the country and journals, and the branch structure of publication activity on the research topic, highlighting academics’ contributions in the PPP research field by the number of citations and published papers. The search for the most relevant publications used the key concept of “private-public partnership” in titles, abstracts, and keywords in the bibliometric database Scopus for 1952 - 2022. VOSviewer software is used to build network maps of keyword compatibility, collaborate with authors by country, and measure the research time. Based on the results of the literature review, conclusions are drawn about the growing dynamics of the number of publications in the PPP field; 3 stages of scientific interest in PPP research are identified; the multidisciplinary nature of the concept under study is confirmed; the PPP term is clarified; 7 clusters that characterize the main areas of research in the field of PPP are identified; the leading countries and the most cited authors are identified by their affiliation to a particular country; it is established that the intensive development of scientific research in PPP in the countries of the world took place in the period 2010-2014; the development of PPP in CEE and Ukraine is characterized.
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 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 | 0.013 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.118 | 0.187 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".