Twenty-five years of the <i>Journal of Small Business and Enterprise Development</i>: a bibliometric review
Bibliographic record
Abstract
Purpose Commensurate with the 25th anniversary of the Journal of Small Business and Enterprise Development ( JSBED ), this retrospective work distils trends across all original articles published in the journal during this time period. Design/methodology/approach Bibliometric analysis techniques are used to analyse 917 original JSBED publications. Specifically, performance analysis is conducted to analyse the journal's publication and citation patterns, bibliographic coupling and author keyword co-occurrence analysis are conducted to identify major themes, and co-authorship analysis is conducted to analyse author collaborations. Findings Results indicate JSBED has grown considerably since its inception, both in terms publication and citations. JSBED 's most prevalent themes include management and growth of small firms, entrepreneurship education, strategy in small firms, business development, technology in small firms, business competencies in small firms, internationalization in small firms, role of social capital, entrepreneurial orientation and entrepreneurship in under-represented and minority populations. Originality/value This is the first comprehensive bibliometric analysis of JSBED in the journal's history. Accordingly, it presents a novel and heretofore disparate understanding of the key themes and dialogues emerging from an established journal with a growing reputation for scholarly and practitioner impact.
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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 | 0.020 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.092 | 0.120 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".