Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 2006.00352

Universities through the Eyes of Bibliographic Databases: A Retroactive Growth Comparison of Google Scholar, Scopus and Web of Science

Enrique Orduna-Malea·Selenay Aytac·Clara Y. Tran
View original source ↗Read source document ↗Export metadata ↓

Abstract

The purpose of this study is to ascertain the suitability of GS's url-based method as a valid approximation of universities' academic output measures, taking into account three aspects (retroactive growth, correlation, and coverage). To do this, a set of 100 Turkish universities were selected as a case study. The productivity in Web of Science (WoS), Scopus and GS (2000 to 2013) were captured in two different measurement iterations (2014 and 2018). In addition, a total of 18,174 documents published by a subset of 14 research-focused universities were retrieved from WoS, verifying their presence in GS within the official university web domain. Findings suggest that the retroactive growth in GS is unpredictable and dependent on each university, making this parameter hard to evaluate at the institutional level. Otherwise, the correlation of productivity between GS (url-based method) and WoS and Scopus (selected sources) is moderately positive, even though it varies depending on the university, the year of publication, and the year of measurement. Finally, only 16% out of 18,174 articles analyzed were indexed in the official university website, although up to 84% were indexed in other GS sources. This work proves that the url-based method to calculate institutional productivity in GS is not a good proxy for the total number of publications indexed in WoS and Scopus, at least in the national context analyzed. However, the main reason is not directly related to the operation of GS, but with a lack of universities' commitment to open access.

Explore related subjects

cs.DL

Keep this discovery

BibTeXRIS

Enrique Orduna-Malea, Selenay Aytac, Clara Y. Tran. 2020-05-30. Universities through the Eyes of Bibliographic Databases: A Retroactive Growth Comparison of Google Scholar, Scopus and Web of Science. https://doi.org/10.1007/s11192-019-03208-7

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

INDRA: A New AI Tool for Exploring Tobacco, Fossil Fuel, and Chemical Industry Archives

Five decades of litigation have disgorged hundreds of millions of pages of formerly secret business records from the tobacco industry, along with documents from the makers of drugs, chemicals, food, firearms, and fossil fuels. Yet these archives have been effectively inaccessible to general-purpose large language models (LLMs) because they have never been compiled into an LLM-readable corpus. Chatbots may be familiar with some of the materials contained in such archives but, with no direct access to the documents, they are vulnerable to hallucination and other defects. Here we introduce INDRA, a research platform designed to remedy such failures by embedding the conventions of archival historiography into a system-level protocol governing every output. The platform federates UCSF's Industry Documents Library, Columbia and CUNY's ToxicDocs, Stanford's SRITA, and other heretofore siloed collections, and provides three interlinked safeguards: (1) a closed evidentiary sandbox confines the model to a user-selected corpus, blocking retrieval from external sources that could introduce bias; (2) real-time provenance tagging marks the boundary between archival evidence and parametric inference; and (3) a system-level protocol enforced by deterministic scripts guides the structure of every output. Together these safeguards prevent the model from conflating "the documents say X" with "I think X" or "I learned X from prior training." The result is an LLM-powered research partner enabling massive multi-archival investigations, a tool whose outputs are designed to be checked rather than trusted, and whose architecture makes the conditions of knowledge production visible and auditable. Three case studies demonstrate the method's analytical value and limitations, including what we call the Heraclitus effect, the steppingstone dilemma, and the gullibility (or mafia) problem.

cs.DL↗
arXiv2026

Wavering Oracles: Selective Updating and Correlated Failures in LLMs and Their Implications for Scientific Workflows

Scientific workflows increasingly use repeated queries, multiple models, and interacting agents. Reliability therefore depends on whether models preserve correct conclusions, accept valid corrections, and contribute errors that a selector can distinguish. Using SycoBench- 600 as a controlled measurement substrate, we evaluate these requirements through selective updating, defined by resistance to misleading suggestions and uptake of correct suggestions. The study covers ten models and 17,055 trajectories. Published models span 13.4 to 71.6 percentage points in selectivity. Under identical local evaluation, Qwen3-4B is selectively adaptive at 45.6 points, Gemma3-4B is destabilized at minus 14.1 points, and SmolLM3-3B follows both correct and wrong explicit suggestions, producing zero selectivity. Matched interventions identify model specific responses to doubt, authority, and explicit advice. Among seven published models, the best reaches 95.3 percent accuracy, plurality reaches 88.6 percent, and the oracle ceiling is 99.8 percent. Mean error correlation of 0.285 reduces seven models to an effective independent count of 2.58. A leave-one-stem-family-out reliability selector reaches 96.2 percent, recovering 67.7 percent of the plurality-to-oracle gap. These results establish selective updating, error diversity, and calibrated adjudication as jointly measurable design targets for multi-model scientific workflows.

cs.DL↗
arXiv2026

Same Problem, Different Field: Cross-Domain Solution Import via Domain-Stripped Computational Fingerprints

The same underlying computational problem is solved across unrelated fields under different names: recursive Bayesian state estimation appears as a "Kalman filter" in control, "Bayesian forecasting" in pharmacokinetics, and "data assimilation" in geoscience. Topical and citation-based scientific embeddings cannot see this shared problem. We distill each paper once into a domain- and method-name-stripped faceted computational fingerprint, a free-text mechanism skeleton plus controlled computational facets. We define a tunable, facet-selectable distance over it. The goal is solution import: surface cross-field pairs solving the same problem, so a bespoke implementation can be swapped for another field's standard, specialized solver. On a benchmark of 18 method families across 109 papers, the skeleton lifts cross-domain retrieval average precision over the abstract from 0.222 to 0.513, and the whole fingerprint reaches 0.557. Strikingly, four trained scientific embedders all fall below plain abstract+TF-IDF: they encode topical and citation similarity, the wrong signal for this task. The gain is the representation: the abstract-to-skeleton swap lifts every embedder, and the pipeline is one cached LLM call per paper plus a cheap embedder. An interventional re-skin / math-edit test shows the fingerprint tracks the computation, not the field. On a 501-paper wild corpus, known twins dominate the top of the ranking (23 of the top 30); with planted pairs excluded from the results, three blind LLM judges rate 3 of the top 5 and 8 of the top 30 pairs genuine import candidates, and 0 of 30 random ones. The human verification is the four executed imports: in one, an open standard solver reproduces a bespoke clinical dosing engine's output. We release the benchmark, the code, and the distillation prompt.

cs.DL↗