The use of trade-related writing in upper secondary vocational education and training
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".