Exploring Faculty Engagement With and Perceptions of the Library When Teaching University Students About Fake News
Bibliographic record
Abstract
A Review of: Alwan, A., Garcia, E., Kirakosian, A, & Weiss, A. (2021). Fake news and libraries: How teaching faculty in higher education view librarians' roles in counteracting the spread of false information. Partnership: The Canadian Journal of Library and Information Practice and Research, 16(2), 1-30. https://doi.org/10.21083/partnership.v16i2.6483 Objective – The authors sought to examine how faculty engage with and feel about the library and librarians when teaching university students about fake news. This follow-up article focuses on survey questions not reported on in the previous article by Weiss et al. (2020). Design – Cross-sectional mixed methods survey. Setting – California State University, Northridge (CSUN) in Los Angeles, California. Subjects – CSUN faculty members, including tenured, tenure-track, and term-contract faculty (e.g. lecturers and adjuncts). Methods – The authors identified survey participants using purposeful-random sampling. The CSUN Office of Institutional Research provided the authors with a randomized subsample of 400 faculty (18.88% of all CSUN faculty). The subsample included an equal number of tenure-track faculty and term contract faculty drawn from across all nine CSUN colleges. The full survey consisted of 28 questions and was divided into four sections entitled: “Demographics,” “Personal Views,” “In the Classroom,” and “Role of the Library.” In this article, the authors focus on the “Demographics” and “Role of the Library” sections, exploring the association between select demographic variables (age range, gender identity, college, and academic rank) and questions 23-27 using cross-tabulation and Spearman correlation. Main Results – The authors received 69 survey responses. The cross-tabulation identified a significant association between respondent’s college and response to question 23 (“Do you use the Oviatt Library's resources/services to teach or inform your students about fake news?”). Faculty from two of the nine CSUN colleges and half of the faculty from the remaining colleges reported never using library resources or services. The correlation analysis revealed a moderate correlation between academic rank (Professor, Associate Professor, Assistant Professor, and Lecturer) and response to question 26 (“Do you feel librarians offer sufficient support related to fake news?”). Lecturers were more likely to agree with question 26, while respondents in higher academic ranks were more likely to disagree or strongly disagree. The correlation analysis identified three pairs of questions with significant correlations. Questions 23 and 24 (“Do you collaborate with librarians to teach or inform your students about fake news?”) were significantly correlated. When respondents selected "Never" in response to question 23, they were more likely to select "Never" in response to question 24. Questions 26 and 27 (“Do you feel the Oviatt Library offers sufficient support related to fake news?”) were also significantly correlated. When respondents selected "Agree" or "Neutral" in response to question 26, they were more likely to select "Agree" or "Neutral" in response to question 27. Finally, questions 25 (“Do you feel the Oviatt Library has reputable trustworthy sources?”) and 27 had a weak correlation. The more strongly respondents agreed with question 25, the more likely they were to select "Strongly Agree", "Agree", or "Neutral" in response to question 27. Conclusion – The results of the study suggest that faculty at CSUN have divergent views about how the library and librarians can help them teach students about fake news. Lecturers agree more often than faculty in higher academic ranks that the librarians at CSUN offer sufficient support related to fake news. Library outreach and awareness programs may be helpful in changing how faculty view the library and librarians.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".