SET: National Accounts of OECD Countries
Bibliographic record
Abstract
Two volumes in the series of National Accounts of OECD Countries annuals relating respectively to the financial accounts and the financial balance sheets of OECD countries. In both volumes, data, based on the System of National Accounts (SNA 1993), are expressed in national currency (in euros for euro area countries). Volume IIIa covers financial accounts of OECD countries and includes financial transactions (both net acquisition of financial assets and net incurrence of liabilities), by institutional sector (non-financial corporations, financial corporations, general government, households and non-profit institutions serving households, total economy and rest of the world) and by financial operation. Data are shown for 23 OECD countries for the period from 1993 to 2004, when possible. Volume IIIb covers financial balance sheets of OECD countries and includes financial stocks (both financial assets and liabilities), by institutional sector (non-financial corporations, financial corporations, general government, households and non-profit institutions serving households, total economy and rest of the world) and by financial instrument. Data are shown for 21 OECD countries for the period from 1993 to 2004 when possible. Countries covered include Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Hungary, Italy, Japan, Korea, Mexico (Vol. IIIa only), Netherlands, Norway, Poland, Portugal, Slovak Republic*, Spain, Sweden United Kingdom and United States.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.024 | 0.051 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.075 | 0.084 |
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".