Research Progress and Knowledge Structure of Inclusive Growth: A Bibliometric Analysis
Bibliographic record
Abstract
Environmental and socio-political challenges of today show that traditional models of economic growth and valuation methods, which are based primarily on financial profitability, are not always optimal, but the concept of inclusive growth is gaining popularity. In January 2018, the Inclusive Growth and Development Index was presented at the 48th World Economic Forum in Davos. But the relatively new concept of inclusive growth and its economic meaning remains insufficiently studied and needs further research. Accordingly, the paper aims at providing a bibliometric overview to determine the current state of scientific production in "inclusive growth". Scopus Database was selected as the primary data source. The scientific literature was searched based on the titles, abstracts, and author keywords with the following search strategy: "inclusive growth". A time span of 10 years was set, and thus, only literature published from 2012 to 2021 was included. To obtain a more comprehensive analysis VOSviewer 1.6.16 software was used for mapping and visualizing bibliometric networks of scientific publications. A study of the geographical affiliation of researchers in this area showed that the most significant number of publications was published by scientists from the USA, India, Great Britain, China, South Africa, Australia, Spain, Italy, Canada, and Germany. The average growth rate of publications in this field is the highest among scientists in Spain, Italy, and China. The interest in the topic is constantly growing. As a result of a bibliometric analysis of 2000 publications indexed by the Scopus database from 2012 to 2021, devoted to the issues of inclusive growth, 8 clusters were identified: environmental problems, role, and opportunities of stakeholders in increasing inclusive growth, population movement under the influence of micro-and macro-environmental factors to achieve sustainable development goals, inequality, analysis of economic and population development factors in the context of achieving sustainable development goals, inclusive growth essence, and parameters, poverty. The issues of regional aspects and mechanisms for attaining inclusive growth goals, as well as issues of regulating and ensuring stakeholders' interests, including issues of communication and promotion of inclusive growth paradigm, risk assessment of implementing inclusive economic principles, and formalization of impact factors remain unexplored.
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 | 0.017 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.141 | 0.171 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".