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Record W2039826934 · doi:10.7202/1025668ar

What is A Network? (And Who is Andrew Lang?)

2014· article· en· W2039826934 on OpenAlexvenueno aff
Nathan K. Hensley

Bibliographic record

VenueRomanticism and Victorianism on the Net · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Cultural Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsPresuppositionTRACE (psycholinguistics)Interpretation (philosophy)HistoricismField (mathematics)SociologyContext (archaeology)EpistemologyObject (grammar)New HistoricismHistoriographyHistoryAestheticsLiteraturePhilosophyArtLinguistics

Abstract

fetched live from OpenAlex

This essay traces the strikingly prolific career of Andrew Lang and places that career in the context of the shifting late-Victorian literary field, which Lang served importantly to shape. The essay introduces Lang’s milieu and re-orients readers to a literary personality who, while known, is only rarely studied in his own right—a detail of reception history the essay explains with recourse to the relational sociology of Pierre Bourdieu and Bruno Latour. Restoring Lang’s “network effect” through historical analysis helps raise a number of conceptual questions, each of which is pursued in the essays of this special issue: such questions include the nature of textual interpretation, the changing outlines of disciplines, the philosophy of historical method, and conceptions of authorship and collaboration in the modern cultural marketplace. Placing Lang back in his proper spot at the center of the late-Victorian networks he helped convene (1) helps historicize our understanding of the modern “field of cultural production” (Bourdieu’s term) in an expanded, protodisciplinary sense and (2) discloses new genealogies of literary and theoretical history. These new genealogies in turn cast altered light on the methodological presuppositions we draw upon to evaluate Lang and his network here. “Theoretical historicism” is the term used to describe approaches that trace such feedback loops between the historical object analyzed and the modern method used to analyze them.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.850
Threshold uncertainty score0.656

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.013
GPT teacher head0.259
Teacher spread0.246 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations6
Published2014
Admission routes1
Has abstractyes

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