A Literature Review on the Research of Circular Economy-Based Green MICE
Bibliographic record
Abstract
The circular economy is a mode of economic development centered on high efficient and cyclic utilization of resources, characterized by low input, high efficiency and low emission. “3R” is the most basic practical operating principle of circular economy. The circular economy integrates the cleaner production and the cyclic utilization of wastes. The green MICE has become one of the important industries of developing circular economy. Ping Hu (2006), Ming-Gui Sun (2006), Wei-Dong He (2009), etc. defined the green MICE respectively. As for the literature review on the researches of green MICE, the foreign scholars and countries focus on the construction of guides to the MICE (Meeting) and of ecological venues, such as Cathy Crisci (2009), the US “Meeting Industry Committee”(2003), Canada National Environment Research Council and Green Meeting Committee, Germany, the UK, France, etc. While in China, we focus on the research of green MICE’s development and approach of practice, such as Ming-Gui Sun & Hong-Yuan Zhang (2006), Cheng Yan (2007), Mei-Liang Cai (2008) and Wei-Dong He (2009), etc. Concluded from the literature review, the research of evaluation system of green MICE will be the future task.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".