Study on the Protection and Utilization Strategy of Coastal Wetland Tourism Resources Based on Multi-Level Regression Analysis and the Path to Achieve Common Prosperity
Bibliographic record
Abstract
Common wealth is the essential requirement of socialism, and it is also the goal that countless people have been relentlessly pursuing for thousands of years, and people have never ceased to earnestly aspire to and relentlessly pursue for equal enjoyment and common wealth. This paper studies the impact of coastal wetland tourism resource protection on the realization of common wealth. It analyzes the tourism resources of coastal wetland from the aspects of economic value and resource protection and utilization strategy. On this basis, a multilevel regression model is used to analyze the impact between the two.The tourism economy of each coastal wetland developed rapidly in 2023, which increased by 65%~97% compared with 2015, implying that the conservation and utilization of tourism resources can lead to economic growth and promote economic development. In the multilevel model, resource protection strategy expenditure (0.070), ecological condition (0.265), strategy realization channel (0.053) and institutional trust (0.166) all show significant effects on the level of common wealth. While the regional level variables GDP per capita, provincial ecological level, and tourism resource utilization have unstable effects on the common wealth of urban residents. Based on the multilevel regression model, this study investigates the influence mechanism of tourism resources protection and utilization strategy on common wealth, which provides a basis for the full development of the positive effect of coastal wetland tourism resources protection and utilization strategy on the realization of common wealth.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".