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Record W4248838084 · doi:10.11647/obp.0144.02

2. The Publisher: Grigorii Blagosvetlov

2018· book-chapter· en· W4248838084 on OpenAlexfundno aff
Nikolai Krementsov

Bibliographic record

VenueOpen Book Publishers · 2018
Typebook-chapter
Languageen
FieldArts and Humanities
TopicLinguistics and language evolution
Canadian institutionsnot available
FundersUniversity of Toronto
KeywordsComputer science

Abstract

fetched live from OpenAlex

In search for answers to these questions, the second chapter follows the life and works of Grigorii Blagosvetlov (1824-1880), the journal’s editor-in-chief and publisher. It was Blagosvetlov who first serialized Florinskii’s treatise in his journal and then released it in book format. And, it was Blagosvetlov, Kremetsov argues, who enticed the young professor to write the treatise in the first place and, to a certain degree, shaped its style and contents. Florinskii’s essays were actually part of a broad campaign waged by the journal and its editor to popularize science and to promote a scientific worldview that sought to understand and eventually to cure the ‘social ills’ plaguing post-Crimean Russia. The propaganda of Charles Darwin’s evolutionary ideas, especially their possible ‘social applications,’ became a particular focus of this campaign, with nearly all of the journal’s core contributors publishing articles, essays, and reviews on the subject. Alas, none of them had adequate training in the natural sciences to explore these questions in depth. This occasionally led to embarrassing incidents and bitter polemics that apparently prompted Blagosvetlov’s invitation to Florinskii to write for Russian Word and the publication of his treatise in four of its 1865 issues. Unlike Galton’s 1865 article based on his original statistical studies of blood relations among ‘British men,’ Florinskii’s treatise was a ‘thought piece’ based on his careful reading and analysis of available literature.

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)0.002
metaresearch head score (Gemma)0.007
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 score0.037
Threshold uncertainty score0.124

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0030.002
Scholarly communication0.0080.006
Open science0.0010.002
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0370.033

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 head0.031
GPT teacher head0.234
Teacher spread0.203 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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