Research Quality and Newsworthiness of Published Articles are Partial Predictors of Journal Impact Factors. A Review of: Lokker, C., Haynes, R. B., Chu, R., McKibbon, K. A., Wilczynski, N. L., & Walter, S. D. (2012). How well are journal and clinical article characteristics associated with the journal impact factor?A retrospective cohort study. Journal of the Medical Library Association, 100(1), 28-33. doi:10.3163/1536-5050.100.1.006
Bibliographic record
Abstract
Objective – Determine what characteristics ofa journal’s published articles can be used topredict the journal impact factor (JIF). Design – A retrospective cohort study. Setting – The researchers are located atMcMaster University, Hamilton, Ontario,Canada. Subjects – The sample consisted of 1,267clinical research articles from 103 evidencebased and clinical journals which werepublished in 2005 and indexed in theMcMaster University Premium LiteratUreService (PLUS) database and those samejournals’ JIF from 2007. Method – The articles were divided 60:40 intoa derivation set (760 articles and 99 journals)and a validation set (507 articles and 88journals). Ten variables which could influenceJIF were developed and a multiple linearregression was run on the derivation set andthen applied to the validation set. Main Results – The four variables found to besignificant were the number of databaseswhich indexed the journal, the number ofauthors, the quality of research, and the“newsworthiness” of the journal’s publishedarticles. Conclusion – The quality of research and newsworthiness at time of publication of a journal’s articles can predict the journal impact factor with 60% accuracy.
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 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 | 0.029 | 0.168 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.015 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".