Research Note: mapping women+ of the Arctic: remapping the narrative of polar exploration
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".