Review Policy Moderates the Correlation Between CiteScore and JIF For BMC Journals
Bibliographic record
Abstract
This article examines the relationship between four review policies (RP) run by different BioMed Central (BMC) journals, namely single-blind peer review (SBPR), double-blind peer review (DBPR), open peer review (OPR), and transparent peer review (TPR), and CiteScore and Journal impact factor (JIF). As of December 12, 2021, BMC publishes 306 journals, of which 14 were discontinued. The final data set was 213 journals with data on CiteScore and JIF. Descriptive statistics and the use of violin and mosaic plots were used. Normality tests were conducted and non-parametric correlation and analysis of variance and Mann Whitney tests were used. Moderation analysis was used to assess the extent of the relationship between CiteScore and JIF. 14 (4.8%) are run with DBPR, and 22 (7.5%), 211 (72.3%) and 45 (15.4%) are run with OPR, SBPR and TPR respectively. Analysis of the final dataset shows that the mean, median, and standard deviation of the CiteScore of the 213 journals are 5.64, 4.80, and 3.65, respectively, while the mean, median, and standard deviation of the JIF of the 213 journals are 4.27, 3.36 and 2.90. In descending order, the CiteScore and JIF across the four RP are highest in DBPR, SBPR, OPR, and TPR. There is a strong positive correlation (Spearman rho = 0.87318, p-value < 8.7e-68) between the CiteScore and JIF of the 213 BMC journals. Mann Whitney test (U = 14771.5, Wilcoxon W = 37562.5, Z = −6.228, p-value < 0.000) shows that there is an evidence of significance median differences between the CiteScore and JIF of the 213 journals. There are significant median differences in the CiteScore in JIF across the four review policies. Finally, the RP moderates the relationship between CiteScore and JIF and between JIF and CiteScore, respectively. This article reveals that the review policies adopted by BMC journals are somewhat related to the journal metrics that measure the impact, prestige, relevance, and acceptability of the respective journals.
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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 | 0.197 | 0.760 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.013 | 0.024 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".