Information Hygiene: a Challenge and an Opportunity for Libraries and Librarians
Bibliographic record
Abstract
This paper explores information hygiene as a crucial framework for fostering a healthy digital lifestyle and promoting overall resilience. It traces the concept's evolution, noting its increasing relevance amid rising challenges like artificial intelligence, misinformation, disinformation, and digital addiction, exacerbated by recent global events. The study integrates findings from two comparative research projects on student information behavior in Czechia and Slovakia, alongside an analysis of Czech national reports on digital addictions. These investigations consistently reveal student concerns regarding digital distress and the negative impact of technology on personal interactions. The paper proposes information hygiene as a comprehensive approach, resting on information literacy, cybersecurity, and digital resilience. It emphasizes that effective information hygiene involves both sound information habits and healthy lifestyle habits that bolster overall well-being. A case study of a new university course on information hygiene demonstrates its practical application. Ultimately, public libraries are identified as key institutions uniquely positioned to champion information hygiene, empowering individuals to navigate the complex digital world with greater resilience.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.019 | 0.028 |
| Scholarly communication | 0.030 | 0.030 |
| Open science | 0.002 | 0.026 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".