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Record W4304175331 · doi:10.1080/09537325.2022.2131513

Training for managers not skilled in Industry 4.0 basis: what is the most suitable content to be covered?

2022· article· en· W4304175331 on OpenAlexaff
Gustavo Tietz Cazeri, Luis Antonio de Santa-Eulália, Milena Pavan Serafim, Izabela Simon Rampasso, Rosley Anholon

Bibliographic record

VenueTechnology Analysis and Strategic Management · 2022
Typearticle
Languageen
FieldEngineering
TopicDigital Transformation in Industry
Canadian institutionsUniversité de Sherbrooke
FundersConselho Nacional de Desenvolvimento Científico e Tecnológico
KeywordsKnowledge managementIndustry 4.0Content analysisInteroperabilityDelphi methodProduct (mathematics)Computer scienceOriginalityDelphiBusinessMarketingSociologyWorld Wide WebQualitative research

Abstract

fetched live from OpenAlex

OpenAlex records an abstract for this work, but it could not be fetched just now.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.017
metaresearch head score (Gemma)0.044
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.091

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.044
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0040.004
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0060.002

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 head0.056
GPT teacher head0.253
Teacher spread0.198 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2022
Admission routes1
Has abstractyes

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