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Record W2567479381 · doi:10.51644/9781771120937

Editing as Cultural Practice in Canada

2016· book· en· W2567479381 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typebook
Languageen
FieldComputer Science
TopicDigital Rights Management and Security
Canadian institutionsnot available
Fundersnot available
KeywordsCultural practiceBiologyEcology

Abstract

fetched live from OpenAlex

This collection of essays focuses on the varied and complex roles that editors have played in the production of literary and scholarly texts in Canada. With contributions from a wide range of participants who have played seminal roles as editors of Canadian literatures—from nineteenth-century works to the contemporary avant-garde, from canonized texts to anthologies of so-called minority writers and the oral literatures of the First Nations—this collection is the first of its kind. Contributors offer incisive analyses of the cultural and publishing politics of editorial practices that question inherited paradigms of literary and scholarly values. They examine specific cases of editorial production as well as theoretical considerations of editing that interrogate such key issues as authorial intentionality, textual authority, historical contingencies of textual production, circumstances of publication and reception, the pedagogical uses of edited anthologies, the instrumentality of editorial projects in relation to canon formation and minoritized literatures, and the role of editors as interpreters, enablers, facilitators, and creators. Editing as Cultural Practice in Canada situates editing in the context of the growing number of collaborative projects in which Canadian scholars are engaged, which brings into relief not only those aspects of editorial work that entail collaborating, as it were, with existing texts and documents but also collaboration as a scholarly practice that perforce involves co-editing.

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)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.142
Threshold uncertainty score0.995

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.005
Science and technology studies0.0340.022
Scholarly communication0.0180.003
Open science0.0020.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0080.001

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 head0.009
GPT teacher head0.223
Teacher spread0.214 · 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 designQualitative
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

Citations8
Published2016
Admission routes1
Has abstractyes

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