Bibliographic record
Abstract
Purpose The purpose of this paper seeks to provide an in-depth overview of a series of fake news information literacy library workshops, which were offered 19 times over the course of 2 years. It examines the results of a fake news game, which was played with a wide variety of audiences. Design/methodology/approach This case study examines workshops offered by two librarians at [name of institution], a major research institution in [city], [country]. It describes the workshops in detail and demonstrates how others may adopt this model. Findings The authors found that while high school students proved to be the most adept at recognizing fake news, the literature suggests that mere exposure to digital media is not sufficient in preparing Generation Z in their digital literacy critical assessment skills. Practical implications Library and information professionals are provided with the tools to adapt this workshop to suit the needs of their respective users. Originality/value This paper examines how a workshop can be adapted to seven unique audiences, spanning from high school students to university alumni. It incorporates the Association of College and Research Libraries framework and the latest literature into informing its practice.
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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 | 0.015 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".