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Record W4410798068 · doi:10.54517/jelp3486

Soft law governance of enterprise data compliance in the context of environmental protection

2025· article· en· W4410798068 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Environmental Law & Policy · 2025
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicDigital Transformation in Law
Canadian institutionsnot available
Fundersnot available
KeywordsCompliance (psychology)Corporate governanceContext (archaeology)BusinessSoft lawAccountingData Protection Act 1998Environmental complianceEnvironmental lawEnterprise data managementLawPublic administrationLaw and economicsPolitical sciencePublic relationsProcess managementSociologyPsychologyGeographyFinanceEnterprise softwareSocial psychologyInternational law

Abstract

fetched live from OpenAlex

<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 “soft law” stays at a static level, which is considered to be a social norm in a pluralistic sense. Foreign countries understand “soft law” 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 “multi-subject participation”. 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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.914
Threshold uncertainty score0.481

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.036
GPT teacher head0.246
Teacher spread0.210 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it