Multidisciplinary Databases Outperform Specialized and Comprehensive Databases for Agricultural Literature Coverage
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
A Review of: Ritchie, S. M., Young, L. M., & Sigman, J. (2018). A comparison of selected bibliographic database subject overlap for agricultural information. Issues in Science and Technology Librarianship, 89. http://doi.org/10.5062/F49Z9340 Abstract Objective – To determine the most comprehensive database(s) for agricultural literature searching. Design – Data collection and analysis was conducted using a modified version of the bibliography method, overlap analysis, chi square tests, and data visualization methods. Setting – An academic library in the U.S. Subjects – Eight commonly used bibliographic databases, including comprehensive agricultural indexes (AGRICOLA, AGRIS, and CAB Abstracts), specialized databases (BIOSIS Previews and FSTA), and multidisciplinary databases (Google Scholar, Scopus, and Web of Science). Methods – The researchers selected three review articles that represented sub-topics within the field of agriculture. Sources listed in the bibliographies of the three review articles were used to build a bibliographic citation set for analysis. Using a modified version of the bibliography method, 90 citations were randomly selected from the above-mentioned citation set. Researchers then turned to the 8 selected databases and searched for all 90 citations in each platform. Search queries were crafted in two ways: unique title strings in quotation marks and combinations of terms entered into the “title”, “keyword”, “journal source”, and “author” fields. Citations were considered to be covered in a database if the full bibliographic record was located using the above-mentioned search strategy. Next, chi square tests were used to evaluate if the expected number of citations from the sample group were found in each database or if the frequency differed between the eight databases. The overlap analysis method provided numerical representation of the degree of similarity and difference across the eight databases. Finally, data visualizations created in Excel and Gephi enhanced comparisons between the eight databases and highlighted differences that were not obvious based solely on the analysis of numerical data. Main Results – Researchers found that comprehensive databases (AGRICOLA, AGRIS, and CAB Abstracts) were not in fact comprehensive in their coverage of agricultural literature. However, the results suggested that CAB Abstracts was more comprehensive than AGRICOLA or AGRIS, particularly in regard to its coverage of the sub-topics “agronomy” and “meat sciences”. However, coverage of the sub-topic “sustainable diets” lagged behind multidisciplinary databases, which may be explained by the fact that the topic is interdisciplinary in nature. The superior coverage of CAB Abstracts over other comprehensive databases is consistent with findings reported by Kawasaki (2004). The analysis of specialized databases (BIOSIS Previews and FSTA) suggested that citations within the scope of the database were covered very well, while those out of scope were not. For instance, the sub-topics “sustainable diets” and “meat science” are out of scope of the biological sciences and thus, were not well covered in BIOSIS. The multidisciplinary databases (Google Scholar, Scopus and Web of Science) provided the most comprehensive coverage agricultural literature. All three databases covered most citations included in the data set. However, researchers noted that all three databases provided weak coverage of trade published items, books, or older journals. Conclusion – The study found that multidisciplinary databases provide close to full coverage of agricultural literature. In addition, they provide the best access to content that is interdisciplinary in nature. Specialized and comprehensive databases are recommended when research topics are within the scope of the database. Also, they best support in-depth projects such as bibliographies or comprehensive review articles.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.105 |
| Open science | 0.000 | 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