Counter-Mapping Systems: Gesturing towards decoloniality in systems mapping
Bibliographic record
Abstract
Map-making is a central practice within systemic design. Systemic design projects routinely involve creating visual depictions of systems that are used to make sense of a systemic challenge and guide interventions. Systems maps, like most mainstream approaches to cartography, hold inherent and often unquestioned power. They foreground some narratives while obscuring others, they shape our understanding of systems and frame courses of action. In our presentation, we draw on discourses from critical cartography and decolonial theory to begin sketching the outlines of a systems mapping practice that grapples with the power relations in which maps are entangled. We advocate for a critical approach to systems mapping that would “gesture towards decoloniality” (Andreotti, 2021) while acknowledging that 1) maps construct knowledge; 2) maps privilege some narratives over others; and 3) maps open possibilities for alternative futures. We have already been exploring critical systems mapping practices in our work with a social finance program funded by the Government of Canada. Through these explorations, we have come to a few departure points from traditional systems mapping: explicit authorship and information sources; plural and participatory narratives; purpose and audience statements; making “ugly” maps; mapping “what ought to/could be”, and more. We will expand on these departure points and their link to critical cartography and decolonial frameworks in our presentation. We hope that these may serve as initial provocations for systemic designers to draw inspiration from and critique as we, as a field, continue to develop decolonial approaches in order to further critical systems mapping practices and methods.
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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 | 0.033 | 0.069 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.013 | 0.057 |
| Scholarly communication | 0.015 | 0.023 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 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".