‘Fake News' in the Context of Information Literacy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.004 |
| 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 it