Fake News and Libraries: How Teaching Faculty in Higher Education View Librarians’ Roles in Counteracting the Spread of False Information
Bibliographic record
Abstract
This paper reports on a survey of faculty members at California State University, Northridge (CSUN) in Los Angeles, California, regarding their attitudes about libraries’ and librarians’ roles in the area of fake news. This study is a continuation of a previous paper that reviewed the origins of fake news and faculty perceptions of the concept. The survey results suggest that faculty members have differing views of how libraries and librarians can help them address fake news. Across disciplines, ages, and genders, faculty members’ views show little belief in the use of the library or librarians to help combat fake news. Notably, only lecturers seem to have a strong view of libraries and librarians playing helpful roles in dealing with the fake news phenomenon. These findings may have future implications for librarians who attempt to address fake news with either their faculty or their students. It may be necessary to develop broader outreach and awareness programs to change traditional conceptions of academic librarians and library services, which are often conflated.
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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 | 0.012 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".