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Record W2144465327 · doi:10.3138/carto.48.4.1696

Lines Underground: Exploring and Mapping Venezuela's Cave Environment

2013· article· en· W2144465327 on OpenAlexvenueno aff
María Alejandra Pérez

Bibliographic record

VenueCartographica The International Journal for Geographic Information and Geovisualization · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicGeographic Information Systems Studies
Canadian institutionsnot available
Fundersnot available
KeywordsSketchPerspective (graphical)Field (mathematics)Context (archaeology)DialecticGRASPMental mappingCartographySociologyGeographyComputer scienceEpistemologyArchaeologyArtificial intelligencePsychologyPhilosophy

Abstract

fetched live from OpenAlex

How do the lines on a map come into being? What stories do they tell? These questions are examined in the context of cave exploration and mapping in Venezuela. Ethnographic analysis focuses on mapping in the field, the translation of notes into final maps, and discourses surrounding these practices. The lines that cave surveyors sketch in field books while traversing underground passages reveal a dialectic between cartographic and exploratory practices. Two key factors shape this dynamic: first, the experience of probing humans “pushing passages” with no obvious end in sight, and second, the coordinated and skilled use of tools by explorers working in teams. Here, humans define both the scale and perspective of cartographic representations. But bodies slithering along passages make lines, too. This paper simultaneously considers lines of lead on paper and traces of bodies in the earth as a way to think of exploration and mapping as emergent engagements. This case powerfully illustrates the ontogenic nature of maps, highlighting the (re)production of social relations that continually brings them into being along with the environment that engenders these engagements. Finally, cave exploration and mapping illustrate the limits of our vision and technologies to grasp and order nature.

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.

How this classification was reachedexpand

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.769
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.000
Scholarly communication0.0010.003
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 head0.037
GPT teacher head0.285
Teacher spread0.248 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations18
Published2013
Admission routes1
Has abstractyes

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