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Record W3160278600 · doi:10.31468/dwr.849

Plain language practices of professional writers in Quebec

2021· article· en· W3160278600 on OpenAlexaffvenueabout
Adeline Müller, Isabelle Clerc, Thomas François

Bibliographic record

VenueDiscourse and Writing/Rédactologie · 2021
Typearticle
Languageen
FieldHealth Professions
TopicInterpreting and Communication in Healthcare
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsPlain languageFeelingTask (project management)Focus (optics)Work (physics)Plain EnglishProfessional writingLinguisticsPsychologyComputer scienceMathematics educationSocial psychologyEngineering

Abstract

fetched live from OpenAlex

This article investigates the plain language practices of professional writers in Quebec, using a survey. We contacted 55 professional writers and asked them to complete an online survey about how they apply plain language in their work, and the type of writing assistance they would find useful. We also asked 40 of those writers to carry out a simplification task to see what kind of simplifications they were actually making.
 If the feelings about the reality of the writers’ work is in line with the literature, opinions on plain language guidelines are not. Most writers in our survey find them useful and precise enough, and this contrasts with reported criticisms of such guides. In the simplification task, we noticed that writers focus on the overall understanding of the text, and not only on some linguistic characteristics (as shown in plain language guidelines). The more experienced the writer, the more changes they will make to visual/structural aspects or relational efficiency. Putting the focus on the reader’s needs is their main concern.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.761
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.170
GPT teacher head0.552
Teacher spread0.382 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2021
Admission routes3
Has abstractyes

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