Librarians and Faculty Are Concerned About Misinformation, But Differ in How to Implement News Literacy in the Classroom
Bibliographic record
Abstract
A Review of: Saunders, L. (2023). Librarian perspectives on misinformation: A follow-up and comparative study. College & Research Libraries, 84(4), 478-494. https://doi.org/10.5860/crl.84.4.478 Objective – To explore academic librarians’ perspectives on misinformation, including how they teach it in the classroom and their perceptions of undergraduate students’ news literacy competence. A secondary objective is to compare academic librarians’ and faculty’s misinformation perspectives using data from the author’s previous study (Saunders, 2022). Design – A Qualtrics-hosted online survey modified from the previous study. Setting – Two electronic mailing lists from the American Library Association (ALA). Subjects – There were 189 respondents. The target population was librarians employed in a college or university with at least some library instruction responsibilities. Methods – The electronic survey was distributed in March 2021. The quantitative analysis included descriptive statistics and chi-squared tests to identify any statistically significant differences in responses between librarians liaising with different departments and between librarians and faculty. Main Results – Academic librarians agree that mis- and disinformation is a major concern. The survey defined misinformation as “inaccurate information shared by accident,” and disinformation as “inaccurate information shared on purpose to mislead/deceive.” In the article, misinformation was used to encompass both terms. The majority of librarians address news literacy during classroom instruction using a variety of methods, including active learning and, less often, using assignments with news literacy outcomes. Librarians who do not teach news literacy report that faculty members do not request this type of instruction, and that they do not have time to teach it. Faculty and librarians agree that misinformation is a concern, and that news literacy instruction is important for combatting misinformation. However, faculty members were more likely to report that misinformation was not relevant to their discipline, and that news literacy instruction should occur elsewhere in the curriculum. Faculty also tended to rate students’ proficiencies in identifying misinformation as higher than librarians. Conclusion – The majority of academic librarians and faculty are concerned about misinformation and agree that news literacy instruction is an important method to address it. It is unclear how librarians are teaching students how to identify misinformation and if they are using evidence based methods to do so. Many faculty members do not include librarians in this instruction or do not believe it should be addressed in their discipline. Based on these results, librarians could provide outreach to faculty members about how librarians can address misinformation within their disciplinary curriculums. They could also provide training workshops to faculty members to enable them to teach these skills on their own.
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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 | 0.011 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".