Academics are Reading More Electronic Journal Articles in More Subjects, Using Varying Strategies to Find and Manage Them
Bibliographic record
Abstract
Objective – To assess how the increase in number of electronic journals available to academic scholars has changed their information-seeking or consulting behaviour, with respect to 1) the amount and diversity of sources they read; 2) strategies they use to keep up-to-date in their fields; 3) use of personalized information services; 4) determining the value and relevance of articles; and 5) personal management of scientific information. This study is a follow-up to an earlier quantitative study (Borrego, Anglada, Barrios, & Comellas, 2007) in the same setting.
 
 Design – Qualitative, using an open-ended questionnaire, followed by personal interviews of a small group of the respondents.
 
 Setting – Universities that are members of the Consortium of Academic Libraries of Catalonia (CBUC), which is made up of the eight public Catalan universities and the National Library of Catalonia, Spain.
 
 Subjects – One hundred thirty-seven scholars from the member universities of diverse ages and disciplines. Eleven of these academics were selected for personal interviews.
 
 Methods – The authors used a two-staged approach to gather comments from researchers on their use of electronic journals. First, an open-ended, self-administered questionnaire (with some pre-testing done) was sent by e-mail to some 490 academics who had provided e-mail addresses in the quantitative study; 137 were returned and analyzed. Personal interviews were then conducted with 11 scholars who had given the most detailed answers in the questionnaire. Their ages ranged from 28 to 67; each was from a different discipline, and from six of the universities.
 
 Informed consent sheets (describing the study and guaranteeing anonymity) were given to the 11 interviewees. Personal interviews were conducted in the subjects’ offices by one of the authors, and lasted between 45-60 minutes. In the interview stage, the authors wanted to examine: use and assessment of the library, access to electronic information, and impact of e-resources on information behaviour. Subjects were also asked to provide three suggestions on improving access to scientific information.
 
 Main Results – The amount of reading and number of electronic journals and other sources consulted among the scholars who participated in this study has increased. Three-quarters of survey respondents consult more journals and read more articles. The scholars reported that they are grateful for the increase in electronic information and its enhanced ease of access, and are not overwhelmed by it. Their reading has become more discriminatory, though, with many reporting “skimming” much of what they read to save time. Scholars keep up-to-date in three main ways: web browsing of journal issues, library database searches, and TOC e-mail alerts. More than 90% of survey respondents reported conducting database searches. Google and Google Scholar were often mentioned ahead of specific library database names. In determining value and relevance of an article, its author and abstract are key for scholars. In addition, personal information management techniques used by scholars were all over the board. The three main methods were use of print or electronic folders, reference management software, and no system. Many of the academics felt their information management systems were “rudimentary” (p. 225).
 
 The request for suggestions and comments on the questionnaire was not answered by “most of the sample” (p. 226). Those who did respond to this request asked for more library resources. The main complaint expressed by scholars concerned the difficulty and complexity of finding journal article content using the Library website (e.g., varying databases, difficulty of interpreting what journal electronic and print holdings are available). Because of this, a number of scholars used Google to find library-subscribed content.
 
 Conclusion – By having greater and easier access to e-journals, scholars accessing the CBUC read more articles from more disciplines. Scholars would prefer a simpler library interface to search for online content. Due to the complexity of finding article content, they use web search tools like Google and Google Scholar to get to what they need faster. The authors of this study believe research should be conducted on the use of the Consortium’s metasearch tool to reduce the complexity. Research should also be conducted on value-added features of search interfaces for particular disciplines.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.146 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".