Bibliographic record
Abstract
Resource taxation has received wide attention in recent years. Government intervention through tax policy instruments is well recognized as essential for optimal exploration of mineral reserves, the maximization of mineral rents, the maintenance of environmental standards, and the creation of incentives for reinvestment of mineral rents. Some industrialized countries have already imposed various kinds of resource tax on natural resources, such as a severance tax in the United States, a mining tax in Canada, and a mine products tax in Japan. China's resource tax system was implemented in 1984 under the Draft Regulations on the Resource Tax. This system levies and collects a tax on enterprises and individuals in China engaged in the extraction of crude oil, natural gas, coal, and metallic and non-metallic products. However, systemic deficiencies in the resource tax system have become apparent as China has pursued its reform and opening-up policy and its economic conditions have changed (Zhang 2007; An & Jiang 2008; Fu 2012; Li and Du 2008; Wang 2010). Although the system was previously reformed in 1993 under the Provisional Regulations on the Resource Tax, the tax system has not been modified again during the past 20 years. Beginning in 2010, the government began a resource tax reform pilot program centered in the Xinjiang Uygur Autonomous Region (hereinafter, 'Xinjiang' or 'autonomous region'). Compared with other areas in China, the western regions possess more abundant resources but have relatively underdeveloped economies and weak financial bases.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".