Web Search Engines - Not Yet a Reliable Replacement for Bibliographic Databases
Bibliographic record
Abstract
A Review of: Bates, J., Best, P., McQuilkin, J., & Taylor, B. (2017) Will web search engines replace bibliographic databases in the systematic identification of research? The Journal of Academic Librarianship, 43(1), 8-17. https://doi.org/10.1016/j.acalib.2016.11.003 Abstract Objective - To explore whether web search engines could replace bibliographic databases in retrieving research. Design - Systematic review. Setting - English language articles in health and social care; comparing bibliographic databases and web search engines for retrieving research published between January 2005 and August 2015, in peer-reviewed journals and available in full-text. Subjects - Eight bibliographic databases: ASSIA (Applied Social Sciences Index and Abstracts), CINAHL Plus (Cumulative Index to Nursing and Allied Health Literature), LISA (Library and Information Science Abstracts), Medline, PsycInfo, Scopus, SSA (Social Services Abstracts), and SSCI (Social Sciences Citation Index) and five web search engines: Ask, Bing, Google, Google Scholar, Yahoo. Methods - A literature search via the above bibliographic databases and web search engines. The retrieved results were independently appraised by two researchers, using a combination of tools and checklists, including the PRESS checklist (McGowan et al., 2016) and took guidance on developing search strategies from the Centre for Reviews and Dissemination (2009). Main Results - Sixteen papers met the appraisal requirements. Each paper compared at least one bibliographic database against one web-search engine. The authors also discuss findings from their own search process. Precision and sensitivity scores from each paper were compared. The results highlighted that web search engines do not necessarily use Boolean logic and in general have limited functionality compared to bibliographic databases. There were variances in the way precision scores were calculated between papers, but when based on the first 100 results, web search engines were similar to some databases. However, their sensitivity scores were much weaker. Conclusion - Whilst precision scores were strong for web search engines, sensitivity was lacking; therefore web search engines cannot be seen as a replacement for bibliographic databases at this time. The authors recommend improving the quality of reporting in studies regarding literature searching in academia in order for reliable comparisons to be made.
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.
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 | 0.251 | 0.583 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.013 | 0.006 |
| Bibliometrics | 0.058 | 0.110 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.031 | 0.061 |
| Open science | 0.009 | 0.013 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.021 | 0.019 |
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".