Digital Literacy Concepts and Definitions
Bibliographic record
Abstract
This article presents a scan of the concept of “digital literacy” and discusses issues encountered in the literature, including: a) challenges in the research base for conceptualizing digital literacy, b) the multiplicity of frameworks which attempt to situate digital literacy but lack sound theoretical origins, and c) wide disagreement among stakeholder disciplines, including education, media studies, library information studies and computing/ICT studies as to what specific skills or knowledge should fall under the umbrella term of digital literacy. The review focuses on the field of education and briefly examines the inconsistent local, national, and international curriculum standards used to both instruct and assess students. It concludes with a presentation of a brief assessment tool, the Software Recognition Test, which preliminary research suggests has predictive validity for educational use and could, with further development, be used for low stakes assessment of digital literacy for K-12 or post-secondary settings.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| 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".