Local Colour : Ghosts, variations
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.028 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".