Back to the Drawing Board: Creative Mapping Methods for Inclusion and Connection
Bibliographic record
Abstract
Abstract The most well-known representation of the globe, the Mercator Projection, often provokes surprise for its considerable distortions: despite appearances, Greenland is almost five times smaller than Canada, and Russia is, in fact, approximately half the size it appears. Since the oldest civilizations, maps have relied on shifting knowledges to become more accurate and efficient, a process accelerated with science and technological development. But the unrealistic proportions of the Mercator map point to a critical reflection: maps show no absolute truths, nor are they neutral. Maps tell stories; they represent ideas as much as spaces, and exactitude is no synonym for neutrality. On the contrary, mapping is a cultural and political act. In the 1990s, geographers started to defy the power relationships of mapmaking with critical cartography. This critique, strongly supported by activists, opened new debates and representational possibilities in which scientific principles started to matter less than social and environmental justice, political participation, and storytelling. Within this framework, this chapter reflects on two alternative mapping methods used in the humanities and social sciences: social cartography and deep mapping. Each section introduces origins, theoretical frameworks, reception, and applications. Because these methods aim to rectify the abuse of power often enabled by scientific mapping, they use non-prescriptive mapmaking to legitimize neglected perspectives. Social Cartography is intrinsically participatory and uses mapping as a collaborative and critical practice. It challenges the role of traditional cartography in socio-political spheres, creating opportunities for new narratives and communities to be heard and understood. Deep maps represent abstract characteristics of a place. They can transcend the boundaries of bi-dimensional and pictorial representation, and consequently, reach different publics. The method is flexible, combining literature and immersive experiences to convey personal or subjective qualities of a place. Other expressions of deep mapping include audio and performative documentations. Social cartography and deep mapping operate against traditional mapmaking by reinforcing the notion that non-institutionalized maps are just as valid in guiding public actions and projects. As participatory practices within communities, these methods promote dialogue, empowerment, and transformation. Therefore, they are indispensable in ensuring democratic research and decision-making.
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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 | 0.019 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.006 | 0.016 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.054 | 0.009 |
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".