Soft law governance of enterprise data compliance in the context of environmental protection
Bibliographic record
Abstract
<p class="MsoNormal" style="text-align: justify; text-justify: inter-ideograph; line-height: 120%; mso-pagination: none; layout-grid-mode: char; mso-layout-grid-align: none; punctuation-wrap: simple; text-autospace: none; margin: 12.0pt 0cm 6.0pt 0cm;"><span lang="EN-US" style="font-size: 10.0pt; mso-bidi-font-size: 11.0pt; line-height: 120%; font-family: 'Times New Roman',serif; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN;">Domestic and foreign countries have different views on soft law governance. In China, the understanding of &ldquo;soft law&rdquo; stays at a static level, which is considered to be a social norm in a pluralistic sense. Foreign countries understand &ldquo;soft law&rdquo; from a dynamic perspective, that is, they mainly tend to take it as a means of governance and a mechanism to solve disputes and contradictions. Combining the above two viewpoints, soft law governance can be understood as a governance concept or governance model, and it takes the needs of the governance object as the starting point. Moreover, it advocates bottom-up governance. This governance method reflects the value connotation of people-oriented, democratic, autonomous and inclusive, and actively practices &ldquo;multi-subject participation&rdquo;. It can be seen that, rather than hard law, soft law is more suitable for the liquidity, development and change characteristics of enterprise data, especially in the context of environmental-related data, and is conducive to condensing the governance force of multiple subjects including the government. The effectiveness of enterprise data compliance governance is of great significance in the field of environmental protection. With the help of ecological environment data such as carbon emission data, environmental detection data and pollution control data, the efficiency and benefit of ecological environment governance can be improved, and the efficiency of ecological governance can be driven by data. But compared with the United States, the European Union and Singapore, the effect of enterprise data compliance soft law governance is not significant. Through literature research and comparative analysis, the factors affecting the effectiveness of Chinese soft law in enterprise data compliance governance are analyzed, and the solutions can be found from the comparative analysis.</span></p>
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 | 0.031 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.020 | 0.013 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".