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Record W4381852640 · doi:10.1002/9781119086130.ch9

I/Y

2023· other· en· W4381852640 on OpenAlexaff
Joseph Grossi

Bibliographic record

Venuenot available
Typeother
Languageen
FieldArts and Humanities
TopicMedieval Literature and History
Canadian institutionsUniversity of Victoria
FundersUniversity of North Carolina at GreensboroJohns Hopkins University
KeywordsComputer science

Abstract

fetched live from OpenAlex

This chapter provides descriptive information about people, places, things, and concepts in Chaucer's works and Chaucer's influence on generations of writers after him, and also an overview of topics of particular significance to Chaucer scholarship. It contains entries that start with the letter “I”, and the subsequent chapters of this book are alphabetized accordingly. This book thus provides a comprehensive overview of the life, times, works, sources/analogues, and influence of Geoffrey Chaucer for a new millennium of general readers, students, and scholars. The entries contain the headword, the name and institutional affiliation of the author of the entry, the body of the entry, often a “see also” section with cross-references to related entries in the encyclopedia, and finally in most cases a list of references, with complete bibliography, that are mentioned as in-text citations in the entry.

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)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.474
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0030.002
Scholarly communication0.0080.005
Open science0.0010.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.5260.494

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 head0.028
GPT teacher head0.209
Teacher spread0.181 · 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.

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
Published2023
Admission routes1
Has abstractyes

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