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Record W3010656605 · doi:10.3138/cart-2018-0027

The Place Names of French Guiana in the Face of the Geoweb: Between Data Sovereignty, Indigenous Knowledge, and Cartographic Deregulation

2020· article· en· W3010656605 on OpenAlexvenueno aff
Matthieu Noucher

Bibliographic record

VenueCartographica The International Journal for Geographic Information and Geovisualization · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicGeographic Information Systems Studies
Canadian institutionsnot available
FundersAgence Nationale de la Recherche
KeywordsToponymySovereigntyIndigenousDeconstruction (building)PoliticsColonialismGeographyPolitical scienceLawArchaeologyEngineering

Abstract

fetched live from OpenAlex

French Guiana, the only overseas region of Europe located in South America, is faced with the claims of identity politics, particularly those of indigenous peoples, who propose alternative place names. This critical analysis of the process of a posteriori recognition of toponyms is based on deconstruction of local, national, and international toponymic databases circulating on the geoweb, supported by interviews with the advocates of these corpora. We propose a critical analysis of toponymic data flows, examining how these data transit through the Web and disappear into the limbo of the Internet or gradually become definitive. This highlights the complexity of the current digital geographic information landscape: national institutes defend a form of data sovereignty for their territory, but they are caught between the digital empowerment of local communities now able to produce counter-cartographies and planetwide cartographic deregulation emanating from the Web giants.

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)0.004
metaresearch head score (Gemma)0.009
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 score0.134
Threshold uncertainty score0.266

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0090.017
Scholarly communication0.0120.008
Open science0.0010.004
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 head0.038
GPT teacher head0.332
Teacher spread0.294 · 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

Citations15
Published2020
Admission routes1
Has abstractyes

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