‘Fake News' in the Context of Information Literacy
Bibliographic record
Abstract
This chapter describes a study that interviewed 18 participants (8 professors, 6 librarians, and 4 department chairs) about their perceptions of ‘fake news' in the context of their educational roles in information literacy (IL) within a large Canadian university. Qualitative analysis of the interviews reveals a substantial overlap in these educators' perceptions of skills associated with IL and ‘fake news' detection. Librarians' IL role seems to be undervalued. Better communication among integral IL educator groups is recommended. Most study participants emphasized the need for incorporating segments dedicated to detecting ‘fake news' in IL curricula. Pro-active IL campaigns to prevent, detect, and deter the spread of various ‘fakes' in digital media and specialized mis-/disinformation awareness courses are among best practices that support critical thinking and information evaluation within the societal context. Two other interventions, complementary to IL as per Rubin's Disinformation and Misinformation Triangle, are suggested – detection automation technology and media regulation.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".