Reference Management Practices of Postgraduate Students and Academic Researchers are Highly Individualized
Notice bibliographique
Résumé
A Review of: Melles, A., & Unsworth, K. (2015). Examining the reference management practices of humanities and social science postgraduate students and academics. Australian Academic & Research Libraries, 46(4), 250-276. http://dx.doi.org/10.1080/00048623.2015.1104790 Objective – To understand patterns in reference management practices of postgraduate students and faculty members at one institution. Design – Mixed methods online survey and semi-structured interviews. Setting – Public research university in Australia. Subjects – The survey included responses from 81 postgraduate students. Semi-structured interviews were conducted with 8 postgraduate students and 13 faculty members. Methods – The researchers distributed an 18-item survey via email to approximately 800 people who previously registered for EndNote training sessions. Survey participants were also recruited via a website advertisement. The researchers recruited postgraduate student interview participants from the list of survey respondents. Librarians invited faculty members to participate in the semi-structured interviews. Interview audio recordings were transcribed and coded for data analysis. Main Results – The survey found that 71.4% (n=55) of respondents used reference management software (RMS) and 29% (n=22) did not. Over half of the students who did not use an RMS described other ad hoc or “manual” (p. 255) methods for organizing and tracking references. The majority of participants reported using EndNote (67.53%, n=52), while few respondents reported using other RMS tools like Zotero (1.3%, n=1) or Mendeley (1.3%, n = 1). Software awareness (49.32%, n=36), recommendations from faculty members (30.14%, n=22), and University support (47.95%, n=35) were the primary motivations for choosing a specific RMS. Other important factors included ease of use (32.88%, n=24) and integration with Microsoft Word (46.58%, n=34). Students preferred RMS features that support the process of accessing and using references in a paper, and reported that technical problems were the most common frustrations. The researchers found that student interview respondents were more likely to use RMS (75%, n=6) than were faculty member respondents (31%, n=4). Interview respondents varied in which RMS features they used, like importing references, PDF management, or “Cite While You Write” plug-ins (p. 258). Few interviewees used the RMS’s full functionality, either due to variations in workflow preferences or lack of awareness. Similar to survey respondents, interviewees who did not use an RMS reported their own personal practices for managing references. The time and learning curve necessary to become proficient with a particular RMS, as well as how the RMS fit into a particular task or workflow, influenced respondents’ decisions about software selection and use. Faculty members were split with their advice to students about using an RMS, with some respondents advocating that an RMS can save time and trouble later in their writing processes, while others took a more cautious or hands-off approach. Conclusion – The authors conclude that measuring RMS use or non-use does not reflect the real world complexity behind student and faculty member reference management practices. They suggest that librarians may want to rethink focusing on RMS as the sole reference management solution. Librarians should also recognize that institutional availability and support may influence users’ RMS choices. A user-centred approach would account for RMS and non-RMS users alike, and librarians should “develop a more flexible perspective of reference management as part of an approach to researchers that aims to understand their practices rather than normatively prescribe solutions” (Melles & Unsworth, 2015, p. 265). Instruction workshops should help students and faculty members select features or systems that match their existing research processes, rather than exclusively demonstrate the mechanics of a particular RMS.
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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 | 0,003 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,622 |
| 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; un appel candidat d’une seule tête enseignante, 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 ».