Bibliographic record
Abstract
With the rapid economic development,countries are faced with the question of how to make the trend analysis,evaluation,forecasting and decision-making for its economic development accurately and timely.Fortunately,the quarter GDP is recognized as key indicator that is reflected the rapid economic development.In this way,it is very critical that quarter GDP data should be accuracy and timeliness.As a result,a growing number of scholars started to look for the quarterly GDP accounting method according to their own national conditions,to make the quarterly GDP accounting and applied research,for timely and accurate manner to develop its own national conditions for economic policy to provide effective services.While the accounting methods differ from one country to another,but the ASEAN,the United Kingdom,Japan,Germany,Canada,France,are more prominent.In this paper,a representative of ASEAN,the United Kingdom and Japan's quarterly GDP accounting methods are made and their advantages and disadvantages of a brief analysis are given.It is useful to carry out this quarter's GDP accounting for our country in the draw.
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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 | 0.021 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.010 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".