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Record W6143347

Complexité de la phrase en langue de spécialité: mythe ou réalité? Le cas de la langue médicale

2006· article· fr· W6143347 on OpenAlexaff
Maurice Rouleau

Bibliographic record

VenuePanace@: Revista de Medicina, Lenguaje y Traducción · 2006
Typearticle
Languagefr
FieldArts and Humanities
Topiclinguistics and terminology studies
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsPhilosophyHumanitiesPhraseLinguistics
DOInot available

Abstract

fetched live from OpenAlex

On one hand, specialized sentences are said to be "complex" because they are "long." On the other, sentences in medical texts, as defined in the present study, are as long as those found in general texts. It would then be tempting to conclude that medical sentences are as complex as general ones. However, this is not the case. Medical sentences compared to general sentences of the same length appear less complex. Their lower complexity relates to a greater use of independent clauses. In addition, subordinate clauses used by physicians are less complex than those used by general writers. Therefore, it can hardly be said that complexity characterizes all specialized sentences. Medical sentences, as defined here, would be an exception.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.853
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.003
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.023
GPT teacher head0.296
Teacher spread0.273 · 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
GenreEmpirical

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

Citations1
Published2006
Admission routes1
Has abstractyes

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