Attitudes & perceptions about the trades as a potential career choice: A Study of Otago University students
Bibliographic record
Abstract
Anecdotal evidence suggests that the trades have long been associated with negative attitudes and stereotypes that have led to a decline in the number of people choosing to undertake a career in the trades. This phenomenon is currently highlighted by the skills shortage in the trades. Following the work of Scharfenburg (2000), who investigated the attitudes of Canadian high school students towards apprenticeships, this research investigated the attitudes and perceptions of students towards the trades as a career option.\nOne hundred and four Otago University students completed a survey indexing their attitudes and knowledge concerning the trades. Results from this survey indicated that students had a limited knowledge of what occupations denote a trade, what skills and training are required to become a tradesperson and finally what financial return and employment prospects can be offered by the trades. It was found that students who had a lack of awareness about careers in the trades were more likely to have increased negative attitudes concerning the trades. Furthermore, these students were less likely to consider the trades as a career option. These results confirm both Scharfenburg (2000)'s findings and anecdotal evidence suggesting that a greater focus needs to be placed on promoting the trades in a manner that increases students' awareness of careers within the trades.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".