Towards a better view of Dutch net foreign assets
Bibliographic record
Abstract
Once every quarter, DNB calculates the amount of Dutch net foreign assets. The IMF has laid down that such calculations must be based to the extent possible on up-to-date figures at market value. Where Dutch subsidiaries abroad and (foreign owned) subsidiaries in the Netherlands are concerned, this requires the use of estimates, because in general only book values are available, which are usually much less up-to-date than market values. When these (in most cases) lower book values are raised to the level of market values, it becomes clear that the Netherlands is richer than one would conclude from the usual figures, in which holdings of equity capital in subsidiaries are included at book value. Over the past decades, the difference repeatedly exceeded EUR 100 billion, sometimes even EUR 200 billion. At the end of 2011, the difference amounted to EUR 70 billion. The present article focuses on the factors determining the difference and on the procedure used by DNB to estimate this difference in order to be able to publish net foreign asset figures at market value. An insight into these issues is all the more important now that European countries have agreed to monitor each other s financial position much more closely using a so-termed macroeconomic scoreboard. The scoreboard features not only the current account balance, but also such quantities as a country s net foreign assets.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".