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Record W4412617561 · doi:10.1080/2154896x.2025.2530277

Research Note: mapping women+ of the Arctic: remapping the narrative of polar exploration

2025· article· en· W4412617561 on OpenAlexaff
Carol Devine, Charlie Hewitt, Tahnee Prior

Bibliographic record

VenueThe Polar Journal · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicArctic and Russian Policy Studies
Canadian institutionsOptech (Canada)
Fundersnot available
KeywordsNarrativeArcticThe arcticPolarGeographyHistoryAstrobiologyGeologyOceanographyLiteratureArtAstronomyPhysics

Abstract

fetched live from OpenAlex

Mapping Women+ of The Arctic (Mapping WoA+) is an interactive and participatory digital cartography project fitting squarely within contemporary polar social science research, methodologies, and dialogue on the history, future, and gender dimensions of the Arctic. This multidisciplinary project – of a region with prevalent colonial and male-dominated stories of discovery, exploration, cartography, place-naming, and impact – shines a spotlight on women+’s (anyone who identifies as a woman) contributions to the Arctic. It highlights their stories within Polar history, especially in the Arctic. Their contributions have largely gone undocumented or are lesser known, as are those of Indigenous peoples and LGBTQ+ individuals. Mapping WoA is inspired by Mapping Antarctic Women, also a crowd-mapping interactive project featuring place names on the continent named for and by women who contributed to Antarctic history and science, and other re-mapping initiatives by Indigenous and other excluded and marginalised groups. While we recognise cartography and mapping conventions are historically settler, colonial, and male-dominated, we envision our project as a part of re-mapping efforts doing more than redrawing lines but contributing to reshaping how we think about land, nature, power, gender, and belonging, and contributing to mapping that creates vs. erases, reclaims and recognises.

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)0.008
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.697
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0050.002
Scholarly communication0.0000.000
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 head0.082
GPT teacher head0.397
Teacher spread0.315 · 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 designQualitative
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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