From Quality to Information Quality in Official Statistics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The term quality of statistical data, developed and used in official statistics and international organizations such as the International Monetary Fund (IMF) and the Organisation for Economic Co-operation and Development (OECD), refers to the usefulness of summary statistics generated by producers of official statistics. Similarly, in the context of survey quality, official agencies such as Eurostat, National Center for Science and Engineering Statistics (NCSES), and Statistics Canada have created dimensions for evaluating the quality of a survey and its ability to report ‘accurate survey data’. The concept of Information Quality, or InfoQ provides a general framework applicable to data analysis in a broader sense than summary statistics: InfoQ is defined as “the potential of a data set to achieve a specific (scientific or practical) goal by using a given empirical analysis method.” It relies on identifying and examining the relationships between four components: the analysis goal, the data, the data analysis, and the utility. The InfoQ framework relies on deconstructing the InfoQ concept into eight dimensions used for InfoQ assessment. In this article, we compare and contrast the InfoQ framework and dimensions with those typically used by statistical agencies. We discuss how the InfoQ approach can support the use of official statistics not only by governments for policy decision making, but also by other stakeholders, such as industry, by integrating official and organizational data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it