More DOIs are Accessed Through Library Discovery Services than Through Google
Bibliographic record
Abstract
A Review of: Wang, X., Cui, Y., & Xu, S. (2018). Evaluating the impact of web-scale discovery services on scholarly content seeking. The Journal of Academic Librarianship, 44(5), 545-552. https://doi.org/10.1016/j.acalib.2018.05.010 Abstract Objective – To examine trends in digital object identifier (DOI) web referrals and explore the referring domains, especially those originating from web-scale discovery systems like ProQuest’s Summon and Primo. Design – Log analysis and web traffic analysis. Setting – CrossRef, a web server that connects DOIs to the corresponding articles’ landing pages. Subjects – Web traffic that passed through CrossRef between 2011 and 2016. Methods – The researchers collected data from CrossRef using a web tool called Chronograph. The data captured information about the websites users were on when they requested a DOI (called the referrer) and about the time and date of each request. The researchers used time series analysis to discover longitudinal patterns in the data. Annual, monthly, and weekly trends were also examined with a seasonal adjustment model, a seasonal trend decomposition, and log transformation. They also isolated traffic from four institutions in Australia, Japan, Sweden, and the United States of America to determine if overall seasonal patterns were reflected locally. ProQuest websites were of particular interest to the researchers because they determined that it had the highest market share of discovery services. Much of the analysis focused on ProQuest’s serialsolutions.com, exlibrisgroup.com, and proquest.com website domains. Main Results – ProQuest servers sent over 25 million DOI referrals through CrossRef – more than either Web of Knowledge (n=24.47 million) or Google (n=15.38 million). Referral traffic grew over the period with the sharpest growth rate occurring between 2011 and 2012. Of ProQuest’s domains, serialsolutions.com (Summon) had more traffic and more growth over the observation period than exlibrisgroup.com (Primo). In all of the years studied, the busiest months were September to November and January to March, while June to August and December were low points. Seasonal fluctuations were attributed to university vacation schedules as demonstrated in the traffic patterns of four ProQuest-subscribing institutions. Weekly trend analysis showed that Monday to Thursday had consistently heavy referral traffic. Of the remaining days, the fewest referrals were observed on Saturdays. Conclusion – DOI referrer traffic is closely tied to the university calendar. Library discovery products are used more frequently to access DOIs than Google.
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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 | 0.004 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.020 | 0.043 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.056 | 0.038 |
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".