Higher Education Fields of Study and the Use of Transferable Skills at Work: An Analysis Using Data from the Programme for the International Assessment of Adult Competencies (PIAAC) in Canada
Bibliographic record
Abstract
Given the rapidly changing job market in Canada and globally, there have been increasing calls to address the transferable skills gap between higher education graduates and the skills needed for the many new and changing jobs across the labour market. To investigate which fields of study in higher education in Canada produce graduates who go on to use more transferable skills on the job, we created an index of transferable skills use at work from several background questionnaire variables available in the Programme for the International Assessment of Adult Competencies (PIAAC) in Canada survey. A series of four least squares linear regression models were used to examine the impacts of variables such as field of study and occupation type on this transferable skills index. Teacher training and education programs were found to have the highest scores on the transferable skills index, suggesting that these programs (and other professional programs) should be considered as a source of inspiration for how all programs can promote transferable skill development in their students. We also found a connection between transferable skill use and management roles within the workplace, suggesting that transferable skills are important factors in promotion to management roles.
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 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".