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Record W2974085651 · doi:10.3138/cart.54.3.2018-0002

Cultures of Enthusiasm: An Ethnographic Study of Amateur Map-Maker Communities

2019· article· en· W2974085651 on OpenAlexvenueno aff
Mike Duggan

Bibliographic record

VenueCartographica The International Journal for Geographic Information and Geovisualization · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicGeographic Information Systems Studies
Canadian institutionsnot available
Fundersnot available
KeywordsAmateurEnthusiasmHobbyEthnographySociologyGeographyData scienceCartographyVisual artsComputer scienceArtPsychologyAnthropologySocial psychologyArchaeology

Abstract

fetched live from OpenAlex

Further attention should be paid to contextualizing the spaces and practices of amateur map-making. Doing so will provide further insight into the ways that maps, mapping epistemologies, and mapper identities emerge in the practices of everyday life. In order to deconstruct the map, and ultimately the power of maps, it is necessary to investigate maps from the bottom up as well as the top down. The motivations of amateur cartographers, the contexts in which map-making takes place, and the technological processes involved are all important factors to consider when examining how and why maps are produced. Empirical ethnographic evidence from a study of OpenStreetMap and humanitarian “mapping parties” is presented here to demonstrate how the often overlooked cultures of amateur map-making offer novel perspectives on who contemporary map-makers are and what motivates them to map. It is shown that amateur map-making is a broad category that includes close-knit hobby communities and more diverse groups of enthusiastic volunteers. As cartography continues to open up and become more accessible through a range of digital mapping technologies, studying these shifts will be important for understanding how and why the role of the map in contemporary life is changing.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.418
Threshold uncertainty score0.752

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0000.002
Open science0.0010.000
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 head0.022
GPT teacher head0.346
Teacher spread0.324 · 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.

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

Citations9
Published2019
Admission routes1
Has abstractyes

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