Некоторые вопросы применения типовой модели производства статистической информации в зарубежных странах
Bibliographic record
Abstract
The article describes basic components of processes and sub-processes of the Generic Statistical Business Process Model - GSBPM. The short characteristic of GSBPM is given. The authors demonstrate in what way application of GSBPM as preferable reference model in national statistical authorities facilitates communication, information exchange and cooperation between national statistical authorities. The article reviews best practices of statistical offices of several foreign countries (Australia, Denmark and Canada) in using GSBPM to solve the issues of harmonization and modernization of statistical activities. In particular, the experience of the Australian Bureau of Statistics (ABS) in tackling a wide range of practical tasks. The adoption of the GSBPM in Statistics Denmark is described on the example of the corporate long-term plan project “Strategy-2015” which strategic objective is the gradual achievement of higher extent of standardization and unification of processes and IT systems. Analysis of the report presented by the Statistics Canada proved how the model can be as a foundation for several statistical programs for ensuring their quality at practical application and identification of those subprocesses for which there is a greater risk of errors.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".