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Record W4382044574 · doi:10.1353/vcr.2022.a900626

Size Matters: George Gissing's Microscript and the Late-Victorian Print Market

2022· article· en· W4382044574 on OpenAlexvenueno aff
Sean Mier

Bibliographic record

VenueVictorian review · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicHistorical and Literary Studies
Canadian institutionsnot available
Fundersnot available
KeywordsHandwritingParagraphGeorge (robot)HistoryLiteratureArtArt historyLawPolitical science

Abstract

fetched live from OpenAlex

Considering George Gissing's manuscripts housed in the Lilly Library at Indiana University, Bloomington, this essay explores how Gissing managed the expectations and pressures of publication by adopting his signature microscopic handwriting in the late 1880s. The miniaturized form draws attention to larger structures through relief—the line, the paragraph, and the page. Likewise, Gissing's microscopic handwriting as a form points outside itself and himself to the exteriorized world of authorship and print culture. H.G. Wells claimed that Gissing's adoption of a microscopic hand allowed him to visually conceptualize and measure the length of a manuscript as he was in the process of drafting it. In this way, Gissing's compositional form that ostensibly standardized word count per page reflects his ambivalent loyalty and hesitant conformity to the reigning system of the three-volume or triple-decker novel, which relied on pure quantity of words as a standard. At the same time, Gissing's compositional format subverts the commodification of literature by limiting writing's scope, stalling the rapid consumption of the text, and forcing print to indeterminably reproduce the logic of the author's microscopic hand. Gissing's handwriting simultaneously represents an acquiescence to treating literary labor as a financial enterprise and a refusal to treat professional novel-writing as an industry.

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)0.004
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: Other · Consensus signal: none
Teacher disagreement score0.063
Threshold uncertainty score0.124

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.009
Scholarly communication0.0050.004
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.011
GPT teacher head0.256
Teacher spread0.245 · 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
GenreOther

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
Published2022
Admission routes1
Has abstractyes

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