Reference Management Practices of Postgraduate Students and Academic Researchers are Highly Individualized
Bibliographic record
Abstract
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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How this classification was reachedexpand
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.622 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".