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Record W2292801681

Creating Environmentalists: Environmental Law, Identity and Commitment

2009· article· en· W2292801681 on OpenAlexaff
Andrew James Green

Bibliographic record

VenueSSRN Electronic Journal · 2009
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Environmental Valuation
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsIdentity (music)Value (mathematics)Environmental lawEnvironmental policyAffect (linguistics)Work (physics)Connection (principal bundle)SociologyLaw and economicsPositive economicsLawEnvironmental ethicsPolitical scienceSocial psychologyEconomicsPsychologyEnvironmental resource managementMathematicsEngineering
DOInot available

Abstract

fetched live from OpenAlex

Progress on environmental issues will require a shift in how individuals view and value their relationship to the environment. This article examines two divergent theories on how individuals' values are formed. One theory stems from the recent work in economics on how identities (including values) are formed and affect choices. It is based in part on a connection between an individual's choice of identity and their desire to further their own well-being. The second theory is based on a view of an individual's values as commitments that she adopts which are unconnected to her own well-being. This article sets out these theories and examines the connection between them and the formation of environmental law and policy including instrument choice and administrative processes.

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)0.003
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.013
Scholarly communication0.0070.008
Open science0.0010.004
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0080.001

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 head0.030
GPT teacher head0.211
Teacher spread0.181 · 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 designNot applicable
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
Published2009
Admission routes1
Has abstractyes

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