Information literacy: an integrated concept for a safer Internet
Bibliographic record
Abstract
This paper aims to provide an overview of some of the most recent developments in concepts and practices associated with information literacy worldwide, revealing the paradox that, while information literacy is a key discipline of the information society and knowledge economy and is well‐understood in its broader sense, it has made little progress educationally, save for a few exceptions in countries such as Australia, the USA, Canada and the UK. Deriving from the authors' background as university professors, the paper concentrates on approaches to promote information literacy in higher education. The paper concludes by pointing to the need to expand the debate on information literacy and how to raise ethical and moral concerns in the use of the Internet and the new technologies. It also explores the potential role that the European Commission eSafe (2003‐2004) programme can play to encourage research and practice on information literacy in its widest sense, as an intrinsic competency in the fight against the effects of disseminating illegal and harmful content through online and other new technologies.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.003 | 0.032 |
| Scholarly communication | 0.014 | 0.023 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".