A retrospective overview of <i>Journal of Enterprising Communities</i>: <i>People and Places in the Global Economy</i> from2007 to 2021 using abibliometric analysis
Bibliographic record
Abstract
Purpose This paper aims to examine the pattern of publications, using a bibliometric analysis of the Journal of Enterprising Communities : People and Places in the Global Economy (JEC) for the period between 2007 and 2021. Design/methodology/approach The study uses relevant bibliometric metrics and procedures. The analysis covers mainly the number of articles published in JEC, most influential years in terms of the number of publications and citations, top productive countries, most prolific authors, most influential institutions, funding institutions, co-authorship trends, keywords co-occurrence, and vital themes of JEC articles between 2007 and 2021. Findings The journal’s influential impact in terms of citations has increased over time, with 83.62% of the published works receiving at least one citation. Léo-Paul Dana has been recognised as the most prolific author by virtue of his contribution of articles in JEC, and the maximum contribution to JEC comes from the USA, followed by Canada and the UK. University of Canterbury, New Zealand and La Trobe University, Australia were the leading contributing institutions. The study identified “indigenous entrepreneurs”, “gender”, “social entrepreneurship”, “education” and “innovation” as contemporary keywords in the study of enterprising communities. These issues present a clear opportunity for research-related topics for the JEC. Originality/value To the best of the authors’ knowledge, this is the first comprehensive piece in the journal’s history that provides a general overview of the journal's major trends and researchers.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | BibliometricsScholarly communication Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.059 | 0.067 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".