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Record W3208709962 · doi:10.3828/eir.2021.28.2.7

Book History without Old Books

2021· article· en· W3208709962 on OpenAlexaff
Michelle Levy, Kate Moffatt

Bibliographic record

VenueEssays in Romanticism · 2021
Typearticle
Languageen
FieldArts and Humanities
TopicDigital Humanities and Scholarship
Canadian institutionsUniversity of AlbertaSimon Fraser University
Fundersnot available
KeywordsRomanceSet (abstract data type)InteractivityRomanticismPeriod (music)Closure (psychology)AestheticsPsychologyArtComputer scienceLiteratureMultimediaPolitical science

Abstract

fetched live from OpenAlex

During the pandemic, the closure of university libraries and Special Collections meant that there were few opportunities for students to interact with the print and manuscript material of the Romantic period. These conditions created an entirely new set of interactions between instructors and students, students and their classmates, and students and their objects of study. To address these new conditions, we created a series of assignments that sought to recreate a sense of the opportunities and pleasures of the sensory experience in an archive; to foster an understanding of both the material history of Romantic literary culture and of material culture more broadly; and finally to connect students emotionally with the objects of their study. This essay reports on the outcomes of these assignments and what they can teach us about attempts to integrate play, discovery, and interactivity with material objects into the study of Romantic writing.

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)0.002
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 score0.139
Threshold uncertainty score0.466

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0030.003
Scholarly communication0.0070.004
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.1390.045

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 head0.042
GPT teacher head0.225
Teacher spread0.184 · 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
Published2021
Admission routes1
Has abstractyes

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