A Quality Framework for Statistical Algorithms
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
The aim of national statistical offices (NSOs) is to develop, produce and disseminate highquality official statistics that can be considered a reliable portrayal of reality. In this context, quality is the degree to which a statistic’s set of inherent characteristics fulfills certain requirements [9]. These requirements are typically set out in a quality framework, which is a set of procedures and processes that support quality assurance within an organisation and is meant to cover the statistical outputs, the processes by which they are produced, and the organisational environment within which the processes are conducted. Many widely accepted quality frameworks related to official statistics exist; for example, see the Australian Bureau of Statistics’ Data Quality Framework [10], the United Nations’ National Quality Assurance Framework [11], Eurostat’s European Statistics Code of Practice [12] and Statistics Canada’s Quality Assurance Framework [13].
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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