Multidisciplinary Databases Outperform Specialized and Comprehensive Databases for Agricultural Literature Coverage
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,094 | 0,343 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,004 | 0,004 |
| Bibliométrie | 0,059 | 0,093 |
| Études des sciences et des technologies | 0,003 | 0,002 |
| Communication savante | 0,028 | 0,029 |
| Science ouverte | 0,003 | 0,012 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,015 | 0,003 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».