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Record W135353528

Codex Event 8: An Australian and British Collaboration of pulp-printing, installation and artists’ books with Sarah Bodman, Paul Laidler, Tim Mosely, Monica Oppen and Tom Sowden 2011-2012

2012· article· en· W135353528 on OpenAlexaboutno aff
Sarah L. Bodman, Tom Sowden, Paul Laidler, Terry Moseley

Bibliographic record

VenueUWE Research Repository (UWE Bristol) · 2012
Typearticle
Languageen
FieldArts and Humanities
TopicCrafts, Textile, and Design
Canadian institutionsnot available
Fundersnot available
KeywordsJungleArt historyPrintmakingPerformance artThe artsArtVisual artsEvent (particle physics)HistoryCartographyGeographyArchaeology
DOInot available

Abstract

fetched live from OpenAlex

A co-authored article by all the artists for Imprint journal, Australia, September 2012.
\n
\nSarah Bodman and Paul Laidler from the Centre for Fine Print Research (CFPR) at UWE, Bristol, UK joined Tim Mosely in Brisbane at Queensland College of Art (QCA, Griffith University, Brisbane ) after the Impact 7 Multi-disciplinary Printmaking Conference in Melbourne in October last year. Tom Sowden and Monica Oppen collaborated by sending instructions from (CFPR) Bristol and Sydney respectively. Our brief was to explore the concepts of the urban jungle, based on Deleuze and Guattari’s theories of smooth and striated space in their publication A Thousand Plateaus. The striated was to be the instructional signs we assimilate and obey each day as we pass through any city, “don’t walk, walk, do not enter, exit, stay behind the line, go back” etc. The smooth was to remove the control, and think of space in the way that the Inuit see their surroundings on an even, unbroken white horizon of snow, or the way that nomads travel without constraint in the desert landscape. For Codex Event 8, to smooth was to bring the jungle into the urban.
\n
\nAs well as the article in Imprint, a full report can be viewed online:
\nhttp://www.bookarts.uwe.ac.uk/codex8_sb12.htm

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)0.001
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.318
Threshold uncertainty score0.998

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 studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.000
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 head0.058
GPT teacher head0.307
Teacher spread0.249 · 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 designNot applicable
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

Citations0
Published2012
Admission routes1
Has abstractyes

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