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Record W4388766876 · doi:10.3138/9781487547790-002

Acknowledgments

2023· book-chapter· en· W4388766876 on OpenAlexfundno aff

Bibliographic record

VenueUniversity of Toronto Press eBooks · 2023
Typebook-chapter
Languageen
FieldArts and Humanities
TopicLiterature and Culture Studies
Canadian institutionsnot available
FundersUniversity of British ColumbiaUniversity of Toronto
KeywordsGeography

Abstract

fetched live from OpenAlex

This book about cultural memory, collective forgetting, and war originated in a moment of confessed ignorance.An accident of wordplay at a conference -see chapter 2 -led me out of my comfort zone of Parisian modernity, product placement, fashion, and Second Empire authors to a nineteenth-century French war on the Crimean Peninsula that I knew virtually nothing about.Baudelaire, Flaubert, Gautier, Sand, and company had little to tell me about this war.I started looking elsewhere, consulting along the way every friend and colleague I knew in French history, literature, art, and cultural studies.I am grateful to them all for their insight, their scholarly references, their encouragement, and their patience.Thank you especially to members of the Nineteenth-Century French Studies Association, who have given me over the years the opportunity to test-drive ideas about the Crimean War in formal sessions as well as informal bars.It was at meetings of the association that I invariably got the best questions, the best answers, and the best youshould-look-ats.I owe a particular debt of gratitude to Michel Pierssens and the late Jean-Jacques Lefrère, who, without knowing it, sparked the wordplay at the Colloque des Invalides that led to this book.A further debt of gratitude goes to Françoise Gaillard and Catherine Soussloff, who convinced me to follow that spark and see where it would lead me.Their readings of early drafts of this book and their advice, like their friendship, were invaluable.Much of this book was written during a pandemic that regrettably limited access to libraries and archives in Paris but happily allowed me to give talks in New Haven, London, and Paris with marvellous colleagues but without jetlag.The feedback and lively discussions were all the more precious at that time of social isolation "in the trenches," as it were.Thank you to Maurice Samuels, Patrick Bray, and Cary Hollinshead-Strick for making this possible.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.828
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.038
GPT teacher head0.200
Teacher spread0.161 · 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 teacher head, 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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