Feminist cartography and the United Nations Sustainable Development Goal on gender equality: Emotional responses to three thematic maps
Bibliographic record
Abstract
Thematic maps facilitate spatial understanding of patterns and exceptions. Cognitive ability, spatial cognition, and emotional state are related, yet there is little research about map readers’ emotions. Feminist critiques of cartography recognize emotion and affect as legitimate experiences on par with quantitative ways of knowing. We conducted an online survey to measure users’ affective states before and after engaging with three thematic map types. The maps showed data from the United Nations Sustainable Development Goal to achieve gender equality, on the proportion of girls and women aged 15 to 49 who have undergone female genital mutilation/cutting. Participants viewed a choropleth, a cartogram, and a repeating icon tile map; completed map‐related tasks; rated certain map qualities; rated their affective states before and after engaging with the maps; and answered open‐ended questions. The maps piqued curiosity and evoked emotions for most users, while some users perceived the thematic maps as clinical or neutral despite the sensitive topic. After viewing the maps, female participants who were affected expressed deeper engagement in their open‐ended comments than males. Traditionally, cartography construes the human experience as male experience and denies or trivializes women's experiences. Our findings corroborate feminist critiques of this disembodiment and entrenched rational rhetoric of maps.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".