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Record W2625697175 · doi:10.3968/9553

On the Value Stand of Environmental Law

2017· article· en· W2625697175 on OpenAlexvenueno aff
Zhifeng Xiao, Qiong Luo

Bibliographic record

VenueCross-cultural communication · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicEnvironmental law and policy
Canadian institutionsnot available
Fundersnot available
KeywordsAnthropocentrismLegislationValue (mathematics)Environmental ethicsEnvironmental pollutionLawEnvironmental lawSociologyLaw and economicsPolitical scienceEnvironmental protectionPhilosophyEnvironmental scienceComputer science

Abstract

fetched live from OpenAlex

It is found from the different perspectives of the analysis on anthropocentrism and ecocentrism that strong anthropocentrism is difficult to get out of the excessive self-consciousness that is arrogant to the natural environment, and that there are inextricable theoretical defects and practical problems in ecocentrism. The confrontation between the two has promoted anthropocentrism to evolve to produce a new and more reasonable value-weak anthropocentrism. Weak anthropocentrism can not only overcome the various drawbacks in strong anthropocentrism and ecocentrism, but also avoid damaging the principal status of human and trigger human respect to natural environment; It can not only help to build advanced and mature criminal legislation on environmental pollution, but also actively guide human to make use of environment rationally; It can not only help effectively punish the criminal behavior of serious pollution of environment, and will not hinder human needs of survival and social development. In the sense, the environmental value of weak anthropocentrism is the best choice for the criminal legislation on environmental pollution.

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 imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0040.038
Scholarly communication0.0090.009
Open science0.0010.004
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.035
GPT teacher head0.370
Teacher spread0.335 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2017
Admission routes1
Has abstractyes

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