Bibliographic record
Abstract
The text below may differ slightly from the actual presentation.The address is based on the assessments presented at Norges Bank's press conference following the Executive Board's monetary policy meeting on 3 November, Inflation Report 3/04 and on previous speeches.Price stability, in the sense of low and stable inflation, is the objective of monetary policy in a number of countries.Historically, the experience of Norway and other countries is that high inflation has resulted in unstable output and employment.In addition, a fall in the price level will often occur in tandem with a downturn.The first inflation target was introduced in New Zealand in 1990.Canada followed in 1991, the UK in 1992, and Sweden and Australia in 1993.A number of other countries have followed suit.In Norway, the Government issued a regulation introducing an inflation target in 2001.Monetary policy in euro area countries and Switzerland is also aimed at price stability, even though this is not referred to as inflation targeting in these countries.Inflation targeting has proved to be particularly appropriate in very open, small and mediumsized economies.Commodity exports play a particularly important role in several of the countries that were the first to introduce inflation targeting.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 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".