MétaCan
Menu
Back to cohort
Record W3217055235

“A Space of their Own?” Professional Women’s Groups in the Alberta Resource Sector

2021· article· en· W3217055235 on OpenAlexaffabout
Alicia Dawn Bjarnason

Bibliographic record

VenueInternational Journal of Gender, Science, and Technology · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicPolar Research and Ecology
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsGrassrootsLegislatureInequalityResource (disambiguation)Space (punctuation)Gender inequalitySociologyPower (physics)Gender studiesWork (physics)Political sciencePublic relationsPoliticsEngineering
DOInot available

Abstract

fetched live from OpenAlex

Throughout the life cycle of a geoscience career gender inequalities still exist, even in the face of legislative and societal change. One response in Alberta Canada is the formation of professional women’s groups. Drawing on feminist geography, the objective of this research was to explore the social relations and power structures involved within the work environment that categorized where women’s groups are created, why they are created, and the strategies used in addressing gender disparities and inequality. This mixed-methods study included an inventory of current groups that exist in Alberta, an online survey to reach professional STEM women within the geoscience community who have been members of one or more professional women’s groups, and in-depth semi-structured interviews with three key actors from an Alberta based group. The information gathered was then supplemented with research performed by two Alberta based grassroots professional women’s groups, the AWSN and GeoWomen. The intended outcomes were to create evidence-based solutions, which in turn will help contribute to concrete solutions to better support professional female geoscientists in Alberta. Keywords: Gender; Gender Inequality; Gendered Space; STEM; Geoscientists; Feminist Geography

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)0.001
metaresearch head score (Gemma)0.001
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 score0.247
Threshold uncertainty score0.542

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.013
GPT teacher head0.266
Teacher spread0.252 · 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
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueInternational Journal of Gender, Science, and TechnologySame topicPolar Research and EcologyFrench-language works237,207