Engineering Faculty Indicate High Levels of Awareness and Use of the Library but Tend to Consult Google and Google Scholar First for Research Resources
Bibliographic record
Abstract
A Review of:
 Zhang, L. (2015). Use of library services by engineering faculty at Mississippi State University, a large land grant institution. Science & Technology Libraries, 34(3), 272-286. http://dx.doi.org/10.1080/0194262X.2015.1090941
 
 Objective – To investigate the engineering faculty’s information-seeking behaviour, experiences, awareness, and use of the university library.
 
 Design – Web-based survey questionnaire.
 
 Setting – The main campus of a state university in the United States of America.
 
 Subjects – 119 faculty members within 8 engineering departments. 
 
 Methods – An email invitation to participate in a 16-item electronic survey questionnaire, with questions related to library use, was sent in the spring of 2015 to 119 engineering faculty members. Faculty were given 24 days to complete the survey, and a reminder email was sent 10 days after the original survey invitation. 
 
 Main Results – Thirty-eight faculty members responded to the survey, representing a response rate of 32%. Overall, faculty had a high level of use and awareness of both online and physical library resources and services, although their awareness of certain scholarly communication services, such as data archiving and copyright advisory, was significantly lower. Faculty tend to turn to Google and Google Scholar when searching for information rather than turning to library databases. Faculty do not use social media to keep up with library news and updates. The library website, as well as liaison librarians, were cited as the primary sources for this type of information. 
 
 Conclusions – The researcher concludes that librarians need to do a better job of marketing library resources, such as discipline-specific databases, as well as other library search tools. Because faculty use web search engines as a significant source of information, the author proposes further research on this behaviour, and suggests more action to educate faculty on different search tools, their limitations, and effective use.
 
 As faculty indicated a general lack of interest in integrating information literacy into their classes, the researcher notes that librarians need to find ways to persuade faculty that this type of integrated instruction is beneficial for students’ learning and research needs. Faculty were aware of the library liaison program, so this baseline relationship between faculty and librarian can serve as an opportunity to build upon current liaison services and responsibilities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.418 |
| Open science | 0.000 | 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".