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Record W4414072957 · doi:10.54517/jelp3871

The differences in environmental psychology within China elucidated by experts from both natural and social scientists

2025· article· en· W4414072957 on OpenAlexvenueno aff
Ning Jiang, Xiangping Jia, Fengying Nie

Bibliographic record

VenueJournal of Environmental Law & Policy · 2025
Typearticle
Languageen
FieldPsychology
TopicCultural Differences and Values
Canadian institutionsnot available
FundersAgricultural Science and Technology Innovation ProgramChinese Academy of Agricultural Sciences
KeywordsValue (mathematics)SustainabilityInstitutionDisciplineVariety (cybernetics)Natural resourceEmpirical researchExperiential learningEnvironmental psychology

Abstract

fetched live from OpenAlex

<p>This study contributes to the empirical research on personal values theory within organizational settings. Through a case study of a group of scientists from China’s national research institution of agricultural sciences, this research examines the association between individuals’ value orientations of egoism, altruism, and the biosphere and their disciplinary backgrounds. According to the results of a questionnaire-based survey conducted among 678 scientists working within the Chinese Academy of Agricultural Sciences (CAAS), this study reveals a strong awareness of and concern for social values related to sustainability. It also shows that disciplinary background impacts individuals’ value orientation. Compared to natural scientists, social scientists at CAAS demonstrate a lower level of value orientation towards altruism and biosphere. The findings advocate moving away from simplistic messages that aim to promote employees’ pro-environmental behavior or from studies that focus solely on a narrow range of experiential factors. It concludes by emphasizing that sustainability transition efforts can promote the corporate greening process through a variety of managerial measures.</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 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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.523
Threshold uncertainty score0.576

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.017
GPT teacher head0.326
Teacher spread0.310 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2025
Admission routes1
Has abstractyes

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