A Content Analysis of Google Scholar: Coverage Varies by Discipline and by Database
Notice bibliographique
Résumé
Objective – To ascertain the coverage by discipline, publication date, publication language, and upload frequency of the scholarly articles found in Google Scholar.
 
 Design – Comparative content analyses.
 
 Setting – Electronic information resources accessible via the internet (both freely accessible and for-fee databases).
 
 Subjects – Forty-seven online databases and Google Scholar.
 
 Methods – The study compared the content of 47 databases (21 Internet resources freely available to the general public; 26 restricted-access databases) covering a variety of subjects with the content of Google Scholar. Each database was assigned to one of the following discipline categories: business, education, humanities, science and medicine, social science, and multidisciplinary. From April through July 2005, researchers generated random samples of 50 article titles from each of the 47 databases and searched the titles on Google Scholar to determine inclusion. 
 
 Related studies were conducted for publication date and publication language analysis, and for the Google Scholar upload frequency study. For the publication date study, random samples from one database (PsycINFO) with a high degree of variability in Google Scholar coverage were searched for 1990, 2000, and 2004. For the publication language study, Google Scholar coverage of PsycINFO articles in English was compared to coverage of PsycINFO articles published in non-English languages. For the upload frequency study, two databases chosen for their high degree of coverage (BioMed Central and PubMed) were monitored to determine how often the new content was uploaded to Google Scholar.
 
 Main Results – This study revealed that content covered by Google Scholar varies greatly from database to database and from discipline to discipline. Of the 47 databases studied, coverage ranged from 6% to 100%. Mean and median values of coverage for all databases were both 60%. The mean discipline category scores varied from the humanities databases at 10% coverage, to the social sciences and education at 39% and 41% respectively, to science and medicine databases at 76% coverage. Mean coverage was 77% for the multidisciplinary databases. Mean coverage of open access journal databases was 95%, freely accessible databases had 84% mean coverage, and single publisher databases had 83% mean coverage.
 
 The publication language study found a bias towards English language publications. As well, a publication date bias was found – coverage of earlier dates was not as thorough as coverage of more recent publications. In the upload frequency study, for BioMed Central and PubMed there appears to be an approximately 15-week delay in the uploading of new material to Google Scholar.
 
 Conclusions – The results of this study serve to alert researchers and information professionals that Google Scholar (in beta test mode at the time of the study) has poor coverage in certain areas. To those with access to commercial databases, this serves as a cautionary tale. To those with a dearth of commercial databases, Google Scholar is a welcome site and can provide at least some information. The researchers state that the search engine itself could make future content studies unnecessary if it decides to make its content collection methodology transparent to users. Upload frequency, Google Scholar’s linking services, the advanced search option, and the “cited by” feature could all be subjects of future studies. For its first year in operation, Google Scholar offers a broad range of discipline coverage with substantial depth in some areas. At the time of the study, Google Scholar was working with libraries and vendors to connect search results to library-licensed full text.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,019 | 0,062 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,014 | 0,062 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,003 | 0,212 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».