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Enregistrement W2776331773 · doi:10.18438/b8wd4g

Early Career Researchers Demand Full-text and Rely on Google to Find Scholarly Sources

2017· article· en· W2776331773 sur OpenAlexaffvenue
Richard Hayman

Notice bibliographique

RevueEvidence Based Library and Information Practice · 2017
Typearticle
Langueen
DomaineComputer Science
ThématiqueWeb and Library Services
Établissements canadiensMount Royal University
Organismes subventionnairesnon disponible
Mots-clésPublishingLibrary sciencePsychologyLongitudinal studyPosition (finance)Face (sociological concept)SociologyMedical educationPolitical scienceSocial scienceComputer scienceMedicine

Résumé

récupéré en direct d'OpenAlex

A Review of: Nicholas, D., Boukacem-Zeghmouri, C., Rodríguez-Bravo, B., Xu, J., Watkinson, A., Abrizah, A., Herman, E., & Świgoń, M. (2017). Where and how early career researchers find scholarly information. Learned Publishing, 30(1), 19-29. http://dx.doi.org/10.1002/leap.1087 Abstract Objective – To examine the attitudes and information behaviours of early career researchers (ECRs) when locating scholarly information. Design – Qualitative longitudinal study. Setting – Research participants from the United Kingdom, United States of America, China, France, Malaysia, Poland, and Spain. Subjects – A total 116 participants from various disciplines, aged 35 and younger, who were holding or had previously held a research position, but not in a tenured position. All participants held a doctorate or were in the process of earning one. Methods – Using structured interviews of 60-90 minutes, researchers asked 60 questions of each participant via face-to-face, Skype, or telephone interviews. The interview format and questions were formed via focus groups. Main Results – As part of a longitudinal project, results reported are limited to the first year of the study, and focused on three primary questions identified by the authors: where do ECRs find scholarly information, whether they use their smartphones to locate and read scholarly information, and what social media do they use to find scholarly information. Researchers describe how ECRs themselves interpreted the phrase scholarly information to primarily mean journal articles, while the researchers themselves had a much expanded definition to include professional and “scholarly contacts, ideas, and data” (p. 22). This research shows that Google and Google Scholar are widely used by ECRs for locating scholarly information regardless of discipline, language, or geography. Their analysis by country points to currency and the combined breadth-and-depth search experience that Google provides as prime reasons for the popularity of Google and Google Scholar. Of particular interest is the popularity and use of Google Scholar in China, where it is officially blocked but accessed by ECRs via proxy services. Other general indexes, such as Web of Science and Scopus, are also popular but not universally used by ECRs, and regional differences again point to pros and cons of these services. Some specialized services are emphasized, including regional tools such as the China National Knowledge Infrastructure, as well as certain broad disciplinary resources, such as PubMed for its coverage of sciences and biomedical information. Researchers report that ECRs participating in this study were less concerned about how they gained access to full-text scholarly information, only that they could access full-text sources. In particular, ECRs do not take much notice of libraries and their platforms, seemingly unaware of the steps libraries take to acquire and ensure access to scholarly information, while viewing physical libraries themselves primarily as study spaces for undergraduate students and not places for the ECR to visit or work. While ECRs occasionally acknowledge library portals and login interfaces, researchers found that these participants mostly ignored these, and that they found discovery services to be confusing or difficult. Concerning social media use, participants identified 11 different platforms used but only ResearchGate was mentioned and used by participants from all seven countries represented. Social media tends to be used directly for keeping track of research trends and opinions and also the work specific researchers are publishing, and indirectly when referred to sites such as ResearchGate to find full-text of a specific article. Facebook, Twitter, and LinkedIn are used occasionally or moderately, but not universally. Researchers highlight regional differences of social media use in China, where ECRs are more likely to connect with other researchers and receive notifications when those researchers publish. The study reports limited information ECRs’ use of smartphones for information seeking. About half of ECR participants reported use of their smartphone for discovering scholarly sources. The advantage smartphones provide includes near-ubiquitous Internet access and therefore the ability to access scholarly materials on the go, though ECRs are less likely to download or read full-text articles via their smartphones. The rate of adoption of smartphone use for scholarly materials varies by country. Conclusion – Early career researchers access scholarly information in a wide variety of ways, with Google and Google Scholar as the preferred starting location, and with social media also proving useful. Ease-of-use and full-text availability are paramount concerns; the spread of open access materials helps fuel the availability of materials, and Google makes these easy to find. Though physical libraries are perceived to be of limited use, the digital access they provide to full-text scholarly sources is still vital even if ECRs do not make the connection between having that important access and the fact that libraries act as buyers and providers of access

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,002
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCommunication savante
Catégories consensuellesCommunication savante
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Théorique ou conceptuel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,692
Score d'incertitude au seuil0,989

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,002
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0010,000
Communication savante0,0120,534
Science ouverte0,0010,001
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,043
Tête enseignante GPT0,282
Écart entre enseignants0,240 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.

Devis d'étudeThéorique ou conceptuel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations2
Publié2017
Routes d'admission2
Résumé présentoui

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