ГРАМОТНІСТЬ У ГАЛУЗІ ДАНИХ: ВИЗНАЧЕННЯ, ПІДХОДИ, НАПРЯМИ ФОРМУВАННЯ
Bibliographic record
Abstract
The article reveals the issues related to the formation of student’s data literacy. Definitions of statistical literacy and their development over time, approaches and ways of literacy formation, as well as the methods of teaching relevant courses are analyzed.Based on the analysis of the UN experts’ definition of data literacy, the content of the European Digital Competence Framework for citizens, the UNESCO Teachers’ ICT Competency Standards and the National Statistics Development Program of Ukraine until 2023, it is found that data literacy is considered one of the important 21st century skills. It is shown that the content of competence in the data field differs depending on what is taken as the basis: focus on working with scientific data, emphasis on education of citizens in the field of data, employers’ requirements for employees, requirements for teachers, students, analysts, etc. Understanding of adults’ data literacy develops over time. Currently, it is not enough to prepare only critical consumers of statistical information, the emphasis is on an effective approach, the ability to produce data, as well as understand the properties of big data, algorithms for processing and presentation to consumers, ethical implications and data privacy issues.An analysis of the experience of the developed countries (Australia, Canada, United Kingdom) on approaches to generating statistical literacy indicates the prospect of isolating different consumer segments and developing several levels of statistical literacy, from basic to advanced; society as a whole must be at a basic level and students, thought’s leaders and decision makers should be at an advanced level.New forms of student’s activity related to data analysis introduced by academics and practitioners are discussed: building art objects and storytelling based on data; shared data collection by citizens through mobile devices, “play with data” using modern data visualization services. Paths of updating statistical literacy courses for Ukrainian sociology students are outlined, based on a synthetic approach and taking into account the barriers that arise during studying quantitative methods courses
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.008 |
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".