Evaluating Academic Journals without Impact Factors for Collection Management Decisions
Bibliographic record
Abstract
Evaluation of academic journals for collection management decisions is made all the more difficult when some journals do not have impact factors as assigned by the Institute for Scientific Information and its Journal Citation Reports. Focusing on science, technology, and medicine journals, this study presents a method of evaluating such nonranked journals. The method is based on finding a comparator journal to the nonranked journal, distinguishing between original research articles and other article types, tracing citations to these two target journals in citing journals, comparing the quality of the citing journals that cite both target journals, and describing the contextual typology of the citations to the target journals. A case study of two medical science journals, the nonranked Annals of the Royal College of Physicians and Surgeons of Canada and the comparator ranked Canadian Family Physician, illustrates the method. This method can help in determining the value of a nonranked journal in relation to a ranked journal.
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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 | 0.145 | 0.448 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.035 | 0.031 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.018 | 0.009 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".