Disruptive processes and skills mismatches in the new economy
Why this work is in the frame
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Bibliographic record
Abstract
Purpose Analysts predict that disruptive technologies, such as artificial intelligence, will have a monumental impact on the world of work in the coming decades, exacerbating existing skills gaps faster than education systems can adapt. This paper aims to review research on the forecasted impact of technology on labour markets and skill demands over the near term. Furthermore, it outlines how social innovations and inclusion can be leveraged as strategies to mitigate the predicted impact of disruptive technologies. Design/methodology/approach The paper engages in an overview of relevant academic literature, policy and industry reports focussing on disruptive technologies, labour market “skills gaps” and training to identify ongoing trends and prospective solutions. Findings This paper identifies an array of predictions, made in studies and reports, about the impact of disruptive technologies on labour markets. It outlines that even conservative estimates can be expected to considerably exacerbate existing skills gaps. In turn, it identifies work-integrated learning and technology-enabled talent matching platforms as tools, which could be used to mitigate the effects of disruptive technologies on labour markets. It argues that there is a need for rigorous evaluation of innovative programmes being piloted across jurisdictions. Research limitations/implications This paper focusses on these dynamics primarily as they are playing out in Canada and similar Western countries. However, our review and conclusions are not generalizable to other regions and economies at different stages of development. Further work is needed to ascertain how disruptive technologies will affect alternative jurisdictions. Social implications While “future of work” debates typically focus on technology and deterministic narratives, this paper points out that social innovations in training and inclusive technologies could prove useful in helping societies cope with the labour market effects of disruptive technologies. Originality/value This paper provides a state-of-the-art review of the existing literature on the labour market effects of novel technologies. It contributes original insights into the future of work debates by outlining how social innovation and inclusion can be used as tools to address looming skills mismatches over the short to medium term.
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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 | 0.002 | 0.001 |
| 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 it