Onsite Research in the U.S. and Canada: Govemental Data Availability in Notrh America
Bibliographic record
Abstract
This paper attempts to reveal the vertical specialization dependence relationship in East Asian countries using the multi country vertical specialization dependence modeling based on the Asian International Input-Output data. Use of multi country model allows us to study the country-wise vertical specialization association that is not possible with the single country model. More over, the multi country vertical specialization dependence modeling, a new approach to study the vertical specialization (imported intermediate goods to produce the export goods), enables us to explain the dependence on domestic intermediate goods and the dependence on other countries as well. The results show that the vertical specialization dependence on total import and group of USA, EU and ROW is high in general among the East Asian countries. However, it is also important to note that the vertical specialization dependence on 9 Asian countries and Hong Kong is relatively high as compared to non-regional countries. Such a situation of vertical specialization dependence in East Asia indicates the strong relationship (in terms of vertical specialization) among the Asian countries.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.025 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".