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Record W2019942902 · doi:10.7771/1481-4374.1436

Authorship, Collaboration, and Art Geography

2010· article· en· W2019942902 on OpenAlexaboutno aff
Martin de la Iglesia

Bibliographic record

VenueCLCWeb Comparative Literature and Culture · 2010
Typearticle
Languageen
FieldArts and Humanities
TopicComics and Graphic Narratives
Canadian institutionsnot available
Fundersnot available
KeywordsComicsObject (grammar)Context (archaeology)Space (punctuation)Field (mathematics)GeographyIdentity (music)HistorySociologyAestheticsArchaeologyArtLinguisticsLiterature

Abstract

fetched live from OpenAlex

In his article "Authorship, Collaboration, and Art Geography" Martin de la Iglesia explores the connection between geographical spaces and works of art, a connection often made, but hardly theorized, by scholars in the field of art geography. He suggests that the link between space and object is established by the creator of the object. A feasible method is devised to determine the creator's geographical identity, which in turn determines which space is assigned to the object. Particularly, the implications of multiple authorship for such a methodology are considered. The procedure is exemplified by a geographical analysis of the comic book series Civil War, which was produced by four main creators from the United Kingdom, Canada, and the USA. This spatial-stylistic analysis results in the conception of the work as a patchwork of geographical influences bestowed by its creators. To successfully interpret the results of such an analysis, it is necessary to view them in the context of the general geographical circumstances of the world of comics.

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)0.003
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesBibliometrics
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.996
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.005
Science and technology studies0.0050.024
Scholarly communication0.0110.009
Open science0.0010.007
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.013
GPT teacher head0.247
Teacher spread0.235 · 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.

Study designObservational
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

Citations1
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueCLCWeb Comparative Literature and CultureSame topicComics and Graphic NarrativesFrench-language works237,207