A multidimensional analysis of Aslib proceedings – using everything but the impact factor
Bibliographic record
Abstract
Purpose – The purpose of this paper is to show that the journal impact factor (IF) is not able to reflect the full impact of scholarly journals and provides an overview of alternative and complementary methods in journal evaluation. Design/methodology/approach – Aslib Proceedings (AP) is exemplarily analyzed with a set of indicators from five dimensions of journal evaluation, i.e. journal output, content, perception and usage, citations and management to accurately reflect its various strengths and weaknesses beyond the IF. Findings – AP has become more international in terms of authors and more diverse regarding its topics. Citation impact is generally low and, with the exception of a special issue on blogs, remains world average. However, an evaluation of downloads and Mendeley readers reveals that the journal is an important source of information for professionals and students and certain topics are frequently read but not cited. Research limitations/implications – The study is limited to one journal. Practical implications – An overview of various indicators and methods is provided that can be applied in the quantitative evaluation of scholarly journals (and also to articles, authors and institutions). Originality/value – After a publication history of more than 60 years, this analysis takes stock of AP, highlighting strengths and weaknesses and developments over time. The case study provides an example and overview of the possibilities of multidimensional journal evaluation.
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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 | 0.015 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.041 | 0.036 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.004 |
| 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".