A bibliometric analysis of the <i>Journal of Membrane Science</i> (1976-2010)
Bibliographic record
Abstract
Purpose – This study aims to examine publication characteristics and development of a science journal Journal of Membrane Science (JMS) with 35 years ' history by bibliometric indicators. Design/methodology/approach – A bibliometric approach was used to identify its document types, impact factor, publication outputs, most cited articles and large contributing countries/territories and institutions. The main indicators included impact factor, CPP (citations per publication), TC2010 (number of citations from one paper’s publication to the end of 2010), C2010 (number of citations in the year of 2010), number of total articles, “single country articles” and “single institution articles”, “internationally collaborative articles” and “inter-institutionally collaborative articles”, “first author articles” and “corresponding author articles”. The annual citations of most cited articles were displayed in a table list. Findings – The two-year citation window used by impact factor is not fair for a journal which had its peak annual citations in the third or more years. JMS would get a better citation performance if impact factor can be calculated for three or four years. Impact factor is affected by the size of its subject categories. JMS showed higher impact factor rankings in both chemical engineering and polymer science category in the early twenty-first century. Furthermore, the G8 (Canada, France, Germany, Italy, Japan, Russia, the UK and the USA) contributed more than a half of the total, with higher CPP. National University of Singapore, University of Twente and Chinese Academy of Sciences were the main contributing institutions. The citation life cycles revealed the impact history of most cited articles. Originality/value – A bibliometric analysis has been carried out to analyze the characteristics of a journal with 35 years ' history. Some improved indicators including TC2010, C2010, TP, SP, CP, FP and RP have been used for the evaluation. This study provides an evidence from JMS to discuss the feasibility and limitations of impact factor.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.090 | 0.128 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".