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Record W3020422063 · doi:10.4000/mappemonde.3386

Story Maps & Co. The state of the art of online narrative cartography

2017· article· en· W3020422063 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMappemonde · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicGeographic Information Systems Studies
Canadian institutionsConcordia University
Fundersnot available
KeywordsNarrativeThe InternetCartographyState (computer science)Computer scienceWorld Wide WebGeographyVisual artsArtLiterature

Abstract

fetched live from OpenAlex

This article offers a comparative analysis of six applications for mapping narratives on the Internet. Based on the life story of a Rwandan refugee, three main families of cartographic applications were identified: simple applications that allow the user to map stories in a standard format (ex: Tripline and Google Tour Builder); more sophisticated applications directly linked to the world of GIS), which allow the user to tell various stories using maps but which also use maps as tools for spatial and temporal analyses (ex.: ESRI Story Maps, MapStory); finally, applications that are more research oriented using narratives as databases whose analyses can help us better understand the places, their personal and intimate geographies, and the structures of the narratives that refer to them (ex.: Atlascine et Neatline).

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.

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
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.669
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
Bibliometrics0.0000.000
Science and technology studies0.0020.002
Scholarly communication0.0000.000
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 head0.031
GPT teacher head0.316
Teacher spread0.285 · 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