A Quality Framework for Statistical Algorithms
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.
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 | 0.043 | 0.098 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.014 | 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".