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Record W131998328 · doi:10.1093/reep/rem017

The Status of Women in Environmental Economics

2007· preprint· en· W131998328 on OpenAlexaboutno aff
Subhra Bhattacharjee, Joseph A. Herriges, Catherine L. Kling

Bibliographic record

VenueReview of Environmental Economics and Policy · 2007
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsnot available
Fundersnot available
KeywordsRepresentation (politics)PublicationProfessional associationAssociation (psychology)Political scienceWomen in sciencePublic relationsSocial scienceSociologyPsychologyGender studiesLaw

Abstract

fetched live from OpenAlex

This article examines the status of women in the environmental economics profession in terms of their representation and impact. Three indicators are used to gauge the status of women in the profession. They are the representation of women in academia in the United States and Canada, the publication profiles of female environmental economists, and the representation of women in the roles of leadership within the professional association and lead journal of the profession. In a survey of schools with graduate programs in environmental economics, we find that female environmental economists are better represented in the faculty of noneconomics departments than in those of economics departments. A study of the publication profiles of women in the profession's main journal, the Journal of Environmental Economics and Management, indicates that women publish fewer articles on average than their male counterparts, and their papers receive fewer citations on average. Women are well represented in the leadership of the Association of Environmental and Resource Economists and also in editorial positions at the Journal of Environment Economics and Management.

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)0.004
metaresearch head score (Gemma)0.012
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: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0030.003
Scholarly communication0.0040.003
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.001

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 head0.036
GPT teacher head0.254
Teacher spread0.218 · 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
GenreReview

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

Citations1
Published2007
Admission routes1
Has abstractyes

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