Зарубежный опыт построения сателлитного счета культуры: методологические основы и практика
Bibliographic record
Abstract
The article reviews and summarizes the international experience of generating culture satellite account. There are results of analysis of methodological reports, developed by the national statistical bureaus and the relevant cultural statistics departments of Australia, Canada, Columbia, Finland, Spain, UK and USA, such as, view on the concept of «culture» for purpose of culture satellite accounts’ construction, description of structure of economics of culture and the set of cultural and creative industries, applied classifications of economic activities and products. On the basis of the existing practice of compiling culture satellite account the author defines three types of it: satellite account with the structure similar to the core national accounts, tables of selected macroeconomic indices; tables that are not presented in the core national accounts. The article mentions a few of them and sets out development direction for culture satellite accounts.
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How this classification was reachedexpand
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.006 |
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; both teacher heads agree on what is shown here.
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".