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Dostoevsky Studies in North America

2021· article· en· W4206042258 on OpenAlexaff
Katherine Bowers, Kate Holland

Bibliographic record

VenueLiterature of the Americas · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicRussian Literature and Bakhtin Studies
Canadian institutionsUniversity of TorontoUniversity of British Columbia
Fundersnot available
KeywordsScholarshipOutreachNarrativePerspective (graphical)Media studiesSociologyHistoryLibrary sciencePublic relationsPolitical scienceLiteratureLawComputer science

Abstract

fetched live from OpenAlex

This article describes the major trends and events in Dostoevsky studies in North America in the past five years. It begins by providing an overview of notable scholarship in the last five years as well as forthcoming: these include works informed by a philosophical perspective, those which deal with narrative form, and those rooted in contemporary discourse, as well as new computational methods. It also discusses works which are aimed at students, teachers, and general readers of Dostoevsky. The article then goes on to provide a discussion of the history and organization of the North American Dostoevsky Society and the public outreach events and scholarly activities that it organizes, including its popular blog, Bloggers Karamazov. It also provides a summary of the transnational online program organized by the Society and other organizations for the 2021 Dostoevsky bicentenary, which include a lecture series and a birthday party. Finally, the article touches on global connections enabled by new technology and the future of Dostoevsky studies in North America, in particular the website of the International Dostoevsky Society and the online transfer and update of the Society’s bibliography into a research portal hosted on that website.

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)0.000
metaresearch head score (Gemma)0.001
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: Review · Consensus signal: none
Teacher disagreement score0.048
Threshold uncertainty score0.095

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0090.006
Scholarly communication0.0030.002
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.027
GPT teacher head0.344
Teacher spread0.316 · 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
GenreReview

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

Citations1
Published2021
Admission routes1
Has abstractyes

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