A multidimensional analysis of Aslib proceedings – using everything but the impact factor
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.048 | 0.072 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it