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Record W4251761650 · doi:10.1163/9789004434974

Different Lives

2020· book· en· W4251761650 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typebook
Languageen
FieldSocial Sciences
TopicData Analysis and Archiving
Canadian institutionsnot available
Fundersnot available
KeywordsBiographyHistoryChinaClassicsCzechState (computer science)Art historyLiteratureArtPhilosophy

Abstract

fetched live from OpenAlex

Internationally acclaimed biographies are almost always written by British or American biographers. But what is the state of the art of biography in other parts of the world? Introduced by Richard Holmes, the volume Different Lives offers a global perspective: seventeen scholars vividly describe the biographical tradition in their countries of interest. They show how biography functions as a public genre, featuring specific societal issues and opinion-making. Indeed, the volume aims to answer the question: how can biography contribute to a better understanding of differences between societies and cultures? Special attention is given to the US, China and the Netherlands. Other contributions are on Australia, Belgium, Canada, the Czech Republic, Denmark, Iceland, Iran, Italy, New Zealand, Spain, and South Africa. "This book represents a much needed breakdown of the history and current status of Biography Studies throughout the world. Any educator teaching a course in higher education that includes Biography Studies should definitely consider this as a major text for inclusion." Billy Tooma, film maker and Assistant Professor, Wessex County College "The rise of biography is the literary event of our time; Hamilton and Renders are its pioneer scholars, and their compelling primer is a must read." Joanny Moulin, Institut Universitaire de France, on Nigel Hamilton and Hans Renders, in: The ABC of Modern Biography (2018) See inside the book

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)0.002
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: Other · Consensus signal: Other
Teacher disagreement score0.149
Threshold uncertainty score0.499

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.004
Scholarly communication0.0110.010
Open science0.0010.007
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.1490.060

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 head0.030
GPT teacher head0.293
Teacher spread0.262 · 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

Citations2
Published2020
Admission routes1
Has abstractyes

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