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Record W2496966068 · doi:10.1017/ccol9780521841672.003

Textual production and textual communities

2009· book-chapter· en· W2496966068 on OpenAlexaboutno aff
Richard Firth Green

Bibliographic record

VenueCambridge University Press eBooks · 2009
Typebook-chapter
Languageen
FieldArts and Humanities
TopicDigital Humanities and Scholarship
Canadian institutionsnot available
Fundersnot available
KeywordsVisual artsCarvingArtHistory

Abstract

fetched live from OpenAlex

Some years ago, when I was teaching in eastern Canada, I attended a lecture, liberally illustrated with slides, given by a local sculptor on his recent visit to Japan. Unsurprisingly, in view of his vocation, he tended to dwell on monumental and architectural subjects rather than landscapes or human figures and for the best part of an hour we were invited to reflect upon the elegant simplicity of Japanese stone-carving and woodworking. At the end of the talk a slide of weathered wooden shingles silhouetted against a slate-gray sky appeared on the screen; it seemed to us a natural complement to the roof trusses and door jambs of the Shinto shrine we had just been contemplating, and we duly gazed at it with reverential awe. Just before the lecturer snapped the lights back on, he informed us that we were looking at the side of a cattle barn on Nova Scotia's Tantramar Marshes, not ten miles away from where we were sitting. All of us, I believe, experienced the same shock to our unreflective compartmentalization of the exotic and the familiar. It was, of course, a cheap trick, but it provided a vivid illustration of how readily we see what we expect to see, of how easily our eyes can be conditioned, or trained, or fooled into seeing only one aspect of a polysemous image. In many ways it is the same with reading. One reader reads Bleak House because it's a good story, another because she enjoys the eccentric characters, a third because he is inspired by its social criticism - are all three, then, reading the same text? The answer of course must depend on how one defines “text,” but from the point of view of the reader-response theorist they would appear to be reading three closely related but nonetheless distinguishable Bleak Houses . What is of most interest to the reader-response theorist, however, is less the potentially limitless variety of such atomistic readings, but the common ground shared by larger groups of readers – what such readers will generally look to find in Dickens’s novel (their so-called “horizon of expectation”).

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)0.006
metaresearch head score (Gemma)0.022
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: Other
Teacher disagreement score0.022
Threshold uncertainty score0.075

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.022
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0070.007
Science and technology studies0.0110.024
Scholarly communication0.0190.019
Open science0.0020.010
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0220.003

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.

Opus teacher head0.052
GPT teacher head0.194
Teacher spread0.142 · 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

Citations9
Published2009
Admission routes1
Has abstractyes

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