Paratextual and "sampladelic" techniques for "committing centonism" in contemporary poetry published in Canada
Bibliographic record
Abstract
This chapter attempts to model a twofold method for reading centos ‒- poems composed wholly of excerpts from other works -‒ in relation to intellectual property (IP) law, in the context of contemporary poetry published in Canada. The method proceeds by inference, in the reading of published poetry’s paratextual matter, and by analogy, in the relating of cento-writing technique to DJ technics of mixing and sampling recorded music. This chapter’s form also tries to model its proposed method, in that a plethora of sometimes extensive quotations composes a fair deal of the argument that follows (in this, I adopt a practice of re-mixing one’s own work that is also found in contemporary poetry). This proposed interpretive approach illuminates how contemporary cento production navigates copyright law and suggests how an understanding of DJ practice enriches the reception and criticism of centos and related found-poetry forms and techniques. Moreover, this interpretive approach shows how authors and publishers need the users’ rights afforded under Canadian copyright law no less than users and consumers do.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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 teacher head, 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".