Scholarly journal usage: the results of deep log analysis
Bibliographic record
Abstract
Purpose To present the latest results of research conducted at University College London as part of the Virtual Scholar Research Programme, investigating the impact of the digital roll‐out of information services to academics and researchers. This is the second study to look at the information seeking behaviour of academics and researchers in regard to digital journal libraries, and concentrates on the users and usage of Blackwell Synergy. Design/methodology/approach Nearly a million users making ten million item requests were investigated employing deep log methods, developed by the authors to provide robust and big picture analyses of digital information consumers and their behaviour. Findings Usage data has been embellished with user data (for 500,000 people), so enabling comparisons to be made between the information seeking behaviour, for instance, of students and staff, academics and practitioners, scientists and social scientists. We believe this is the first time this type of analysis has been attempted with logs. Of particular note is the “repeat visitor” evaluation and the analysis of one and a quarter million search sessions which categorised sessions in terms of how “busy” they were for a whole range of user groups. Research limitations/implications Demonstrates a powerful and new method, deep log analysis, for mapping and evaluating information seeking behaviour. Practical implications Important data for publishers to enable them to target their services more effectively Originality/value Probably the first analysis of its type, hence showing an aspect of information seeking not previously seen.
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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 | 0.006 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".