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Record W7058197551

Local Colour : Ghosts, variations

2012· article· en· W7058197551 on OpenAlexaboutno aff

Bibliographic record

VenueDiVA (Linnaeus University) · 2012
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
Fundersnot available
KeywordsNarrativeRealmRendering (computer graphics)NovellaLocal color
DOInot available

Abstract

fetched live from OpenAlex

LOCAL COLOUR : Ghosts, variations is a collaboration between In Edit Mode Press and Canadian poet Derek Beaulieu. The publication takes as its point of departure, Paul Auster’s novella Ghosts, and, in particular, Derek Beaulieu’s reworking of Auster’s text, Local Colour. Focusing on the tension created in Beaulieu’s manuscript between the textual narrative and the graphical mark, and the opening it seems to provide toward a realm of intermediality and experimentation, we have solicited a series of textual, aural, oral, musical, and other interpretations, as well as more machinic ‘utilisations,’ of Beaulieu’s manuscript. What interests us, in particular, is the way in which Local Colour seems to split Auster’s narrative text open, deterritorialising it by rendering it graphical and freeing it up, by the same gesture, to a potential excess of meaning. Seeking to extend and amplify this ambitious project, what we attempt with this volume, is to open Beaulieu’s project up for others to split open. We seek to deterritorialise the coloured rectangles of his manuscript – in every sense a violent yet affirmative gesture – and explore the horizons toward which such violence might take us. In doing so, Local Colour: Ghosts, variations collects and counterposes a wide array of strategies and approaches. It is an ambitious, vigorous collection that oscillates and moves between textual narrative, graphical mark, and aural impression, exploring these different realms while rendering uncertain any easy distinction between them.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.944
Threshold uncertainty score0.903

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0970.000

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 head0.011
GPT teacher head0.218
Teacher spread0.207 · 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.

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

Explore more

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