Exploring the relationship between DDI, SDMX and the Generic Statistical Business Process Model
Bibliographic record
Abstract
The UNECE and the Conference of European Statisticians Steering Group on Statistical Metadata (better known as "METIS") have recently developed the "Generic Statistical Business Process Model" (GSBPM).This model has already been widely adopted by national statistical organisations around the world, and is intended to facilitate the convergence of statistical production processes, both within and between organisations.There are certain obvious similarities between the GSBPM and the DDI 3 Combined Life Cycle Model.At the same time, there is growing interest in official statistics in using DDI 3 in the earlier phases of the statistical production process (particularly for microdata), perhaps in combination with SDMX (Statistical Data and Metadata eXchange) standards, which are seen as more appropriate for macrodata.This paper highlights the work so far on exploring the relationships and interoperability between DDI, SDMX and the GSBPM, as a way of modernising and standardising (i.e., "industrialising") statistical production.It was presented at the 2 nd Annual European DDI Users Group Meeting in Utrecht, Netherlands, in December 2010.
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 | 0.008 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".