Bibliographic record
Abstract
In the process of dealing with trade frictions in china,the trade or government organizations such as Ministry of Commerce have played the leading role.Meanwhile the customs is only in charge of providing related statistical data,monitoring and making early warnings for import and export commodities.The change of situations requires the change of customs involvement in dealing with trade frictions,and china customs is ready for the change.Therefore,it's our obligation to put customs into the trade friction-countermeasure-systems and enable it of more power of discourse.By making full use of the ambiguity of the wording of law in the present legal framework,we can look for the channels in different stages of dealing with trade frictions that customs can find its way into.The current functions of the customs should be actively integrated and its non traditional functions should be expanded.The generous practices will be the material basis for the upgrade of customs position,thus laying the groundwork for the final realization of establishing the jurisprudence foundations.
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 | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.052 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".