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Record W7124921123 · doi:10.4401/jgsg-48

Perceiving gendered organizations: positions, power, and gender in geoscience

2025· article· W7124921123 on OpenAlexaboutno aff
Samuel Heimann

Bibliographic record

VenueJOURNAL OF GEOETHICS AND SOCIAL GEOSCIENCES · 2025
Typearticle
Language
FieldSocial Sciences
TopicCareer Development and Diversity
Canadian institutionsnot available
FundersEuropean Commission
KeywordsInequalityDominance (genetics)Relation (database)PerceptionPerspective (graphical)Citizen journalismGender inequality

Abstract

fetched live from OpenAlex

This article explores inequality in European geoscience organizations through the perspective of geoscience women professionals and their perception of gendered positions in academia and industry. Male dominance in geoscience organizations has previously been demonstrated within US and Canadian organizations, often in relation to gender inequality in STEM subjects and rarely in relation to the specific ideals and practices that shape geoscience. The current study contributes a European context, as well as a comparative approach to gendered positions in the organizational contexts of academia and industry. Using participatory research methods and visualization techniques, the study collected 42 organizational maps of academic and industry organizations in 16 European countries. The results reveal perceptions of gender inequality in academic and industrial geoscience organizations through women’s limited access to positions of power, i.e. women geoscience professionals perceived underrepresentation in senior management positions in industry and in senior positions in academic organizations. Within the growing demand for geoscience expertise in the green transition, the results raise questions about what the perceived structures of gender inequality mean in relation to sustainable employment and good working conditions in European geoscience.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.499
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0040.003
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.034
GPT teacher head0.317
Teacher spread0.284 · 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.

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

Citations1
Published2025
Admission routes1
Has abstractyes

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