Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".