Recent Trends in Economic and Business History: The Analysis of the Most Impactful Articles Published from 2016 to 2020
Bibliographic record
Abstract
The article aims to analyse recent trends in economic and business history from 2016 to 2020 and identify the factors determining the impact of the research articles. The authors analysed the ten most cited articles published in prestigious international peer-reviewed journals on economic and business history. They summarize the content of every article and explain its contribution to the field. The article defines efficient theoretical instruments for studies in the field of economic and business history. The observations can be useful to researchers concerned about the impact of their publications and those interested in the dynamics of the field. The synthesis of the observations revealed the following most important factors determining the impact of research articles. The proposal of a new theory or concept certainly raises the interest of the academic community. A new representative empirical material is crucial to increase the level of citations in the field of economic history. Alternatively, a good strategy is to present a new efficient method for the analysis of existing datasets. Explicit explanation of how the new data and methods specify conventional perceptions about historical events and processes increases the popularity of publications. An effective way to raise the interest of the readers is to look at the deviations from the “norms” – historical anomalies and paradoxes. A critical synthetic review of the literature on a topic or a problem is usually appreciated by the community of economic historians, but it is especially popular in journals on business history. The final part of the article offers observations on the disciplinary and national diversity of author teams. It concludes that such diversity results in the multidisciplinary character of the studies and increases their value for the sister disciplines.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".