Bibliographic record
Abstract
In 1997 Statistics Norway took the initiative to a project aimed at improving the methods for calculating price indices of capital goods (durables) for national accounts purposes. Part 1 of the project was a descriptive study of methods used to handle quality changes for capital goods in the EU/EFTA member states, and some overseas OECD countries (USA, Canada, Australia and New Zealand). Information was collected on the methodology used to compile the Producer Price Index (PPI), Consumer Price Index (CPI) and External Trade Price Indices. Part 2 will probably take place in 1999 and 2000, and will be a normative study building on the results and discussions from part 1 of the project. Part 2 will be concluded by a final report to be published in year 2000 1 . The general impression is that more resources are allocated to produce the CPI than the PPI, while least resources are allocated to the production of external trade indices. This priority on the CPI may reflect the higher interest in society at large for this index, compared to other indices. For external trade indices most countries are using the relatively simple unit value method where data already are collected to produce external trade statistics. Chaining combined with resampling is more common for the CPI than for the PPI and external trade price indices. For the PPI the most common period of resampling and reweighting is every 5 th year. For the CPI the most common methods to handle quality changes are overlap pricing and judgmental adjustment. Cars and personal computers are the commodities for which explicit methods are most used. For the PPI the most common method is also overlap pricing. A few countries are using hedonic methods for handling quality and the method is mostly used in calculating sub-indices in the CPI.
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 | 0.005 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".