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.
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 | 0.003 | 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 | 0.003 | 0.007 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".