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“A Garden of Forking Paths”: Reimagining Dreiser (Dreiser’s Path: A View With a Modern Lens. Edited by Irina V. Morozova. Moscow: RSUH Publ., 2023. 206 p.)

2023· article· en· W4389316644 on OpenAlexaboutno aff
Georgii Eduardovich Martirosian

Bibliographic record

VenueLiterature of the Americas · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicSociopolitical Dynamics in Russia
Canadian institutionsnot available
Fundersnot available
KeywordsTributePerspective (graphical)Art historyHistorySociologyLiteratureArtVisual arts

Abstract

fetched live from OpenAlex

The review focuses on the collective monograph Dreiser’s Path: A View with a Modern Lens (2023). The scholarly work reevaluates Dreiser's significance as a classic of American literature and offers a contemporary perspective on his legacy through the lens of modern methodologies such as critical race theory, postcolonial theory, gender theory, etc. The attention of prominent researchers in American literature and culture from Russia, the USA, Canada, and the UK is drawn to Dreiser's contradictory and hermetic view of society and its artistic projection. The monograph reevaluates Dreiser's creative legacy, considering both major and well-known works as well as minor forms. The researchers featured in the monograph highlight the reasons why certain laws operate in Dreiser's artistic world, align Dreiser's value system with the social norms prevailing during his time, typologize the components of Dreiser's texts, and theorize them using contemporary methodological tools. They also juxtapose Dreiser's texts with other artifacts of American culture and demonstrate the evolution of Dreiser's perception from the times of the USSR to the present day. The monograph was conceived in 2021 as a special tribute to Theodore Dreiser to mark his 150 anniversary.

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)0.005
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: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.010
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.005
Science and technology studies0.0040.010
Scholarly communication0.0080.019
Open science0.0010.004
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0040.002

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.

Opus teacher head0.017
GPT teacher head0.297
Teacher spread0.280 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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