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Record W7114890071 · doi:10.5194/ica-abs-10-57-2025

Deep mapping cartography's limits: the artfulness of rendering spatial practice

2025· article· en· W7114890071 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAbstracts of the ICA · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicGeographic Information Systems Studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsRendering (computer graphics)Deep learningVisualizationSpatial analysis

Abstract

fetched live from OpenAlex

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?

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.771
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.017
GPT teacher head0.285
Teacher spread0.268 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it