MétaCan
Menu
Back to cohort
Record W1965278477 · doi:10.3138/6qpx-0v10-24r0-0621

Maps and Journeys: An Ethno-methodological Investigation

2005· article· en· W1965278477 on OpenAlexvenueno aff
Barry Brown, Éric Laurier

Bibliographic record

VenueCartographica The International Journal for Geographic Information and Geovisualization · 2005
Typearticle
Languageen
FieldSocial Sciences
TopicGeographic Information Systems Studies
Canadian institutionsnot available
FundersEconomic and Social Research CouncilEngineering and Physical Sciences Research Council
KeywordsCognitive mapConversationRoad mapSet (abstract data type)Plan (archaeology)Reading (process)Mental mappingCognitionComputer scienceSociologyVisual artsPsychologyCartographyLinguisticsGeographyCognitive psychologyCommunicationArt

Abstract

fetched live from OpenAlex

The notion of the “cognitive map” has long been central to studies of maps, wayfinding, and navigation. In this article we provide an alternative approach to studying map use that re-situates these activities as shared social and cultural practices. The article draws on ethno-methodology and conversation analysis to study video of two examples of naturally organized map reading. We explore how journeying with maps is part of the in situ organization of matters such as workplace tasks, means of transportation, having a “nice day out,” maintaining friendships, and so on. In our first clip, a saleswoman consults an A–Z map while stopped at traffic lights in order to plan the journey ahead. In the second clip, a group of friends consult a map as they set off for a day trip together in a car. These clips provide thick descriptions of the detailed activities involved in map use.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0050.005
Science and technology studies0.0130.012
Scholarly communication0.0050.007
Open science0.0020.008
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.075
GPT teacher head0.387
Teacher spread0.312 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations130
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueCartographica The International Journal for Geographic Information and GeovisualizationSame topicGeographic Information Systems StudiesFrench-language works237,207