Highly cited articles in social sciences: an analytical study
Bibliographic record
Abstract
Purpose This paper aims at analyzing the distinctive characteristics of highly cited articles (HCAs) in the domain of Social Sciences with respect to chronological growth pattern, productive journals, authorship pattern, prolific authors, top institutions and leading countries, network among institutions and top ranked keywords in social science research. Design/methodology/approach The required data has been retrieved from Scopus indexing database and further refined using various limits like document types, subject coverage and total citations, and finally, 839 articles were selected for detail analysis. A set of bibliometric indicators were used to make a quantitative analysis, whereas VOSviewer software tool was used to visualize the institutional network and keywords mapping of the HCAs. Findings This study revealed that highest number of HCAs (371) were published during the decade 2001–2010. Degree of collaboration, collaborative index and collaborative coefficient were observed to be 0.513, 1.98 and 0.988, respectively. The highly cited papers were emanated from 397 journals, contributed by 1,556 authors from 1,326 institutions placed in 46 countries. Social Science and Medicine was the most productive journal; J. Urry of Lancaster University, UK, was the most influential author; the USA, the UK and Canada are the torchbearers in social science research. The paper entitled “Five misunderstandings about case-study research,” authored by B. Flyvbjerg, published in 2006 in Qualitative Inquiry, received highest 4,730 citations. Originality/value The primary value of this paper lies in extending an understanding of the characteristics of HCAs in the domain of social sciences. It will provide an insight to the researchers to get acquainted with the most influential authors, journals, institutions, countries and major thrust areas of research in social sciences.
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 | 0.006 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.037 | 0.047 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".