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Conclusion

2007· book-chapter· en· W6143287 on OpenAlexaboutno aff
Kieran Dolin

Bibliographic record

VenueCambridge University Press eBooks · 2007
Typebook-chapter
Languageen
FieldArts and Humanities
TopicPostcolonial and Cultural Literary Studies
Canadian institutionsnot available
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

The intersection of law and literature remains a vital subject of study in contemporary culture. In December 2000 a high school student in eastern Ontario, who had been bullied by his classmates, read out a creative writing assignment in which the protagonist made preparations for bombing his school. The student was placed in juvenile detention for thirty-four days, and was finally charged with making threats. Leading Canadian writers, including Margaret Atwood and Michael Ondaatje, protested on his behalf, arguing that his imprisonment violated freedom of expression. The case was controversial, with the school and police denying that the boy's story was the cause of his detention. Rather, they argued, he had made verbal threats against fellow students. However, a police search found no weapons or explosives at his home, and the charges against him were dropped. Other students were charged with assaulting him. Apart from its freedom of speech issue, the story illustrates how writing may occupy a contested territory, how it may be subject to the jurisdiction of both literature and law. Using the terminology of Wittgenstein discussed in our Introduction, we could say that the authorities in the case failed to identify the kinds of sentences or the ‘language-game’ performed in the student's text, mistaking an imaginative scenario for a criminal intention. The episode suggests the need for greater recognition of the role of literary activity in our cultural conversation.

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)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.137
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0070.004
Open science0.0010.003
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.1370.030

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 head0.048
GPT teacher head0.203
Teacher spread0.155 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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
Published2007
Admission routes1
Has abstractyes

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