Creating an Understanding of Data Literacy for a Data-driven Society
Bibliographic record
Abstract
Society is become increasingly reliant on data, making it necessary to ensure that all citizens are equipped with the skills needed to be data literate. We argue that the foundations for a data literate society begin by acquiring key data literacy competences in school. However, as yet there is no clear definition of what these should be. This paper explores the different perspectives currently offered on both data and statistical literacy and then critically examines to what extent these address the data literacy needs of citizens in today’s society. We survey existing approaches to teaching data literacy in schools, to identify how data literacy is interpreted in practice. Based on these analyses, we propose a definition of data literacy that is focused on using data to understand real world phenomena. The contribution of this paper is the creation of a common foundation for teaching and learning data literacy skills.
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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 | 0.017 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.004 | 0.035 |
| Scholarly communication | 0.016 | 0.030 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 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".