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Record W2901702355 · doi:10.1515/bis-2018-0018

Artificial Intelligence, Jobs and the Future of Work: Racing with the Machines

2018· article· en· W2901702355 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.

Bibliographic record

VenueBasic Income Studies · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Economy and Work Transformation
Canadian institutionsArup Group (Canada)
Fundersnot available
KeywordsBasic incomeRetrainingUnemploymentGovernment (linguistics)Work (physics)WorkfareScheme (mathematics)EconomicsLabour economicsEngineeringEconomic growthMarket economyWelfare

Abstract

fetched live from OpenAlex

Abstract Artificial intelligence is rapidly entering our daily lives in the form of driverless cars, automated online assistants and virtual reality experiences. In so doing, AI has already substituted human employment in areas that were previously thought to be uncomputerizable. Based on current trends, the technological displacement of labor is predicted to be significant in the future – if left unchecked this will lead to catastrophic societal unemployment levels. This paper presents a means to mitigate future technological unemployment through the introduction of a Basic Income scheme, accompanied by reforms in school curricula and retraining programs. Our proposal argues that such a scheme can be funded by a special tax on those industries that make use of robotic labour; it includes a practical roadmap that would see a government take this proposal from the conceptual phase and implement it nationwide in the span of one decade.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.474
Threshold uncertainty score0.772

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
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 head0.021
GPT teacher head0.287
Teacher spread0.266 · 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