MétaCan
Menu
Back to cohort
Record W4403362283 · doi:10.1080/13636820.2024.2414323

The use of trade-related writing in upper secondary vocational education and training

2024· article· en· W4403362283 on OpenAlexaff
Isabelle Rioux, Rachel Bélisle, Frédéric Saussez

Bibliographic record

VenueJournal of Vocational Education and Training · 2024
Typearticle
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsVocational educationTraining (meteorology)PsychologyMathematics educationPedagogyGeography

Abstract

fetched live from OpenAlex

In Upper secondary vocational education and training (VET) we note the presence of students with learning challenges, including reading and writing difficulties. However, both the learning and practice of a trade require the use of trade-related writing. One characteristic of those writings is their plurisemioticity which we can see as different semiotic resources at hand to do things and make meaning in the situation. The purpose of the present contribution is to describe the use of trade-related writing in upper secondary vocational training (VET) within a heterogeneous group of individuals in terms of their level of education upon entering the training program. Our results from analysis of data from a case study of a carpentry programme shows that learners use such writing with an affective attitude of openness and that they are inclined to use at the first place semiotic systems and mediums with which they are more familiar. Results raise as well issues of information literacy, particularly in terms of the quality of the information obtained by learners in digital medium.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.688
Threshold uncertainty score0.390

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.060
GPT teacher head0.298
Teacher spread0.239 · 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 designQualitative
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
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Vocational Education and TrainingSame topicSecond Language Learning and TeachingFrench-language works237,207