Deep mapping cartography's limits: the artfulness of rendering spatial practice
Bibliographic record
Abstract
Cartesian cartographies, writes Michel de Certeau (1984), make action legible by substituting trace for practice; alas, the "gnostic drive" (92) to capture everyday navigations "causes a way of being in the world to be forgotten" (97).Deep mapping (see McLucas 2000; Biggs 2010; Bissell and Overend 2015; Roberts 2016; Modeen and Biggs 2020) resists preemptive definition for it is through its practice that deep mapping becomes articulated as an apparatus of investigation.For me, deep mapping is situated, embodied inhabitation as a practice of ongoing and open-ended dialogue with the world.Deep mapping does not render down to a map in the sense of a Cartesian cartography, yet neither does it "counter cartography".Deep mapping is not defined through opposition so much as marked by iterative acts of interference with hegemonic forms of representing place, producing geographic knowledge, and rendering spatial research public.How might we render situated spatial practices like deep mapping without flattening, georeferencing, and vectorizing experiential knowledge?If mapping itself is taken to be a mode of immanent inquiry (Knight 2021), how might theorizations developed through spatial practice be recorded while centering the generativity of cartographic process?
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.114 |
| Scholarly communication | 0.016 | 0.022 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".