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Record W2944855091 · doi:10.1108/jgr-11-2018-0079

Disruptive processes and skills mismatches in the new economy

2019· article· en· W2944855091 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueJournal of Global Responsibility · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Economy and Work Transformation
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsDisruptive innovationEmerging technologiesWork (physics)Disruptive technologyEmerging marketsMatching (statistics)BusinessMarketingEconomicsEngineeringComputer science

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.210
Threshold uncertainty score0.157

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.293
Teacher spread0.284 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it