The lining of maps: mapping the intimacies of the cartographic processes
Bibliographic record
Abstract
Each mapping process is affected by the personal lives of the cartographer(s). In this paper we propose to focus on this particular aspect of mapmaking: How do the intimate and emotional aspects of the map's creation impact the decision process and by extension the cartographic outcome? This intimate aspect of mapmaking can become eminently political when the mapmakers uncover the traumatic experiences they face because of their racial, class, gendered or intersectional identities. This paper proposes to investigate the potential of sensibility mapping to represent such dimensions of mapping processes. Sensibility mapping can be defined as a reflexive research-creation approach. It emerged a few decades ago from artistic, feminist, activist and participatory urbanism cartographies. In recent years, this specific mode of representation has been mobilized in a research project to address the mapping complexity related to traumatic genocide stories: the life stories of Rwandan exiles who have lived in Montreal since the 1994 genocide against the Tutsi. Overall, this research underpins sensibility mapping to challenge cartographic norms from an epistemological point of view, in order to enrich the knowledge about how we make 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 | 0.011 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.009 | 0.033 |
| Scholarly communication | 0.017 | 0.021 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.002 | 0.003 |
| 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".