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Record W4388023912 · doi:10.1111/manc.12461

Frictions and the diffusion of automation

2023· article· en· W4388023912 on OpenAlex
Nikolaos Charalampidis

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

VenueManchester School · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Growth and Productivity
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsEconomicsProductivityInflation (cosmology)WageLabour economicsNew Keynesian economicsWelfareCapital (architecture)Keynesian economicsValue (mathematics)UnemploymentProduction (economics)Business cycleMacroeconomicsMonetary policyMarket economy

Abstract

fetched live from OpenAlex

Abstract This paper studies the implications of business cycle frictions for the diffusion of permanent changes in automation. Incorporating task‐based production in different versions of the New Keynesian model reveals considerable short‐run implications. Price‐distorting nominal rigidities amplify the labor displacement and attenuate the productivity and welfare gains of automation during the transition to the new equilibrium. They exacerbate the falls in the labor income share, the job finding probability, the value of long‐term contracts, and labor market tightness. The inflation response follows a J‐curve. Frictions in capital supply and wage rigidities amplify the labor displacement and attenuate the productivity gains too.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.225
Threshold uncertainty score0.718

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.023
GPT teacher head0.200
Teacher spread0.177 · 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