Training and Skills Development Policy Options for the Changing World of Work
Bibliographic record
Abstract
The literature on the changing world of work in the age of disruptive technologies is growing, demonstrating both the interest in and urgency of the issue. Drawing on an in-depth literature review, this article offers a critical assessment of the current state of empirical knowledge about what labour market training after disruption might look like. We also present results of a jurisdictional scan of current labour market training programs in Canada. We then examine the extent to which current policy practices regarding education and training are informed by existing research. We find that the “futurist” work has offered some predictions about expected macro changes in the workforce, including polarization of jobs, job destruction, and the scope and depth of disruption in both the global North and global South. Other research provides some insights into promising programs and policies. However, empirical analyses of these programs - with attention to the changing landscape of work - is limited. In addition, little is known empirically about the track record of success of current education, training, and social programs to adapt to and respond to the new world of work. Finally, alignment between the limited existing empirical research and programs that are currently being delivered to address the changing nature of work is tenuous at best. Thus, policy makers need to redouble efforts to invest in research as to who works and what works in this new technological era in order to respond effectively to anticipated labour force disruptions.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.028 | 0.003 |
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".