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Record W2594231941 · doi:10.3138/cart.52.1.3790

The Practice of Mapmaking: Bridging the Gap between Critical/Textual and Ethnographical Research Methods

2017· article· en· W2594231941 on OpenAlexvenueno aff
Edoardo Boria, Tania Rossetto

Bibliographic record

VenueCartographica The International Journal for Geographic Information and Geovisualization · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicGeographic Information Systems Studies
Canadian institutionsnot available
Fundersnot available
KeywordsEthnographyCreativityNarrativeSociologyDeconstruction (building)Representation (politics)Field (mathematics)CartographyPoliticsEpistemologyHumanitiesGeographyArtAnthropologyLiteraturePolitical sciencePsychologySocial psychologyPhilosophyLaw

Abstract

fetched live from OpenAlex

The recent shift from representation to practice within map theorization has led to a renewed interest in mapmaking calling for both closer attention to the practices involved and the employment of ethnographic methodologies in researching how maps come to life. A deep understanding of the making of maps, however, requires a combination of different approaches, from the critical (text-oriented) to the ontogenetic (practice-oriented), from deconstruction to narrative ethnography, and from cultural contextual readings to subjects-centred readings. Two map scholars with very different backgrounds (phenomenology and political geography) seek to put these different approaches into action while investigating mapmaking through a single case study. The life and work of Laura Canali, who has created maps for Limes: Rivista Italiana di Geopolitica (the leading publication in Italy in the field of international relations) since 1993, are analyzed to show how ethnography and critical reading are better used as complementary rather than conflicting approaches. Comparison of the methodological framework applied here with more traditional approaches employed in the field of historical cartography provides evidence of present-day changes in mapmaking and calls for an enhancement of new practice-oriented methods of analysis. Finally, additional insights from creativity studies suggest that there is an interesting line of research on “cartographic creativity” to be further developed.

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)0.026
metaresearch head score (Gemma)0.016
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Scholarly communication
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.861
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0260.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0170.005
Scholarly communication0.0040.003
Open science0.0010.000
Research integrity0.0000.001
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 head0.125
GPT teacher head0.537
Teacher spread0.412 · 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; both teacher heads agree on what is shown here.

Study designTheoretical or conceptual
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

Citations14
Published2017
Admission routes1
Has abstractyes

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