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Record W6987074440

RUHUMAN: The Typewriter Art of Keith Armstrong

2022· article· en· W6987074440 on OpenAlexaboutno aff

Bibliographic record

VenueLincoln Repository (University of Lincoln) · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicGraphic Design and Typography
Canadian institutionsnot available
Fundersnot available
KeywordsPoetryFinchReading (process)Performance artMinimalism (technical communication)Work of art
DOInot available

Abstract

fetched live from OpenAlex

A monograph of Keith Armstrongs Typewriter Art and visual poetry with essays by Tom Gill, Nicola Simpson, and Barrie Tullett.Barrie Tullett and Tom Gill’s masterful book on Keith Armstrong opens up a splendid oeuvre for a new generation of reader-viewers. Keith Armstrong’s brilliant inventiveness is an essential part of the concrete poetry movement and can now at long last be fully appreciated. This book will be essential reading for artists, designers, and typographers.Jeremy AdlerWith the twenty-first century resurgence of interest in typewriter art, the publication of RUHUMAN by Keith Armstrong is reason for celebration. The work is a joyous and loving homage to a man whose work engages with sound, pattern, colour, geometry, type, and life. There’s peace and minimalism here. There’s also noise and clutter. It sates me and makes me hunger for more.Amanda EarlKeith Armstrong was one of the more underrated exponents of the 1960/70s British poetry revolution. Experienced in retrospect, his complete work comes as a revelation. Following trails embarked upon by his heroes Bob Cobbing, Dom Sylvester Houédard, and Henri Chopin, Armstrong’s work is restless, engaging, and constantly innovative. For a decade back then he caught the zeitgeist. And as a bonus what he created has aged wonderfully.Peter Finch

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)0.000
metaresearch head score (Gemma)0.001
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: Empirical · Consensus signal: none
Teacher disagreement score0.048
Threshold uncertainty score0.161

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0050.003
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0480.012

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 head0.149
Teacher spread0.138 · 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
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

Citations0
Published2022
Admission routes1
Has abstractyes

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