Decolonizing local planning through new social cartography: making Black geographies visible in a plantation context in Colombia
Bibliographic record
Abstract
Maps produced during the saga of European ‘discovery’ were shown to erase local forms of spatial knowledge of colonised populations to serve domination interests. This paper explores the continuation of this colonial erasing logic in local planning practices in Jamundí, a municipality where Black peasants’ traditional farms persist in a sugarcane dominated landscape. We first compare official maps from the current Land Use Plan of Jamundí with social cartography produced by afro-descendant community councils to analyse the maps’ selections, omissions and additions. Through community map drawings and collective discussions during cartography workshops, interviews and tours of the territory, we then reconstruct a Black geography that is concealed in official maps. Our analysis shows that official maps naturalise a scale in which only plantations are formally represented, rendering invisible small-scale traditional agricultural systems and Black ecologies, favouring the expansion of uses and activities detrimental to Black territorial projects in Jamundí. We argue that afro-descendant living spaces and experiences are visually omitted from spatial representation in the physical planning maps through institutionalised processes. We conclude that decolonising local planning is crucial for the recognition and securing of afro-descendant customary land and territorial rights in Colombia as well as for regional sustainability.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.000 | 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".