Bibliographic record
Abstract
Map Worlds plots a journey of discovery through the world of women mapmakers.The journey starts in the "golden age of cartography" in the sixteenth-century Low Lands and ends with tactile maps in contemporary Brazil.As developers of resources that allowed early map ateliers to flourish through marital liaisons, women had an unmistakable role.Others, working from the margins, produced maps to record painful tribal memories or sought to remedy social injustices in the nineteenth century.In contemporary times, one woman produced a revolution in the way we think about continents, likened to the Copernican revolution.Still others created order and wonder about the lunar landscape, while others turned the art and science of making maps inside out, exposing the hidden, unconscious, and subliminal "text" of maps.Promoting social justice and making maps work for the betterment of humanity are goals shared by all these outstanding map-makers.The enthusiasm for the topic grew from my meeting Dr. Eva Siekierska of Ottawa in 1993.During a coffee break at a committee meeting (unrelated to cartography), she told me about her excitement at chairing the International Cartographic Association's newly established Commission on Gender and Cartography.Her enthusiasm aroused my curiosity about the connection between gender and cartography, and for four years thereafter,
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.480 | 0.235 |
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".