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Record W2280683721 · doi:10.1017/s0959269515000277

Michael Zimmermann, Expletive and Referential Subject Pronouns in Medieval French. (Linguistische Arbeiten, 556.)Berlin: de Gruyter, 2014, x + 246 pp. 978 3 11 037337 0 (relié), 978 3 11 036747 8 (numérique, PDF), 978 3 11 039430 6 (numérique, EPUB)

2015· article· fr· W2280683721 on OpenAlexaff
Éric Mathieu

Bibliographic record

VenueJournal of French Language Studies · 2015
Typearticle
Languagefr
FieldArts and Humanities
TopicHistorical Linguistics and Language Studies
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsSubject (documents)LinguisticsPhilosophyHumanitiesComputer scienceLibrary science

Abstract

fetched live from OpenAlex

Michael Zimmermann, Expletive and Referential Subject Pronouns in Medieval French. (Linguistische Arbeiten, 556.)Berlin: de Gruyter, 2014, x + 246 pp. 978 3 11 037337 0 (relié), 978 3 11 036747 8 (numérique, PDF), 978 3 11 039430 6 (numérique, EPUB) - Volume 26 Issue 2

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: Review · Consensus signal: Review
Teacher disagreement score0.053
Threshold uncertainty score0.106

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.005
Science and technology studies0.0040.003
Scholarly communication0.0040.009
Open science0.0010.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0300.014

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 head0.287
Teacher spread0.257 · 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
GenreReview

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
Published2015
Admission routes1
Has abstractyes

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