Port economics, management and policy studies (2009–2020): a bibliometric analysis
Bibliographic record
Abstract
Abstract This paper analyses published research in port economics, policy and management (port studies) based on examining all relevant academic journal papers published from 2009 to 2020. The systematic review of all 1227 papers relies on quantitative and qualitative bibliometric tools to reveal the structures of the research community (i.e., authors’ country of affiliation, number of authors involved, and international collaboration rates) and the themes and content of port research (i.e., research approaches, units of analysis, ports and commodities examined, levels of research localisation, port markets commonly (not) studied). It also presents a taxonomy of port studies based on a content classification of the themes and sub-themes examined. The paper concludes with a citation analysis that reveals the coherence of port research. The analysis is enriched by comparing the findings with similar studies focusing on the 1997–2008 timeframe. This unique monitoring of a period that expands over a quarter of a century offers a valuable tool for better understanding the research landscape and deciding directions for future research. From a theoretical perspective, the study provides evidence of the rapid transformation of port economics, policy and management into a mature research field.
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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 | 0.018 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.216 | 0.291 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".