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Record W1947733994 · doi:10.1002/9781118802366.ch6

Recent Trends in Manufacturing Innovation Policy for the Automotive Sector

2015· other· en· W1947733994 on OpenAlex
P.V. Galvin, Elena Goracinova, David A. Wolfe

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

Venuenot available
Typeother
Languageen
FieldEconomics, Econometrics and Finance
TopicRegional resilience and development
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsAutomotive industryCommercializationIndustrial organizationGermanBusinessPosition (finance)Relevance (law)ManufacturingManufacturing engineeringEngineeringMarketingPolitical scienceFinance

Abstract

fetched live from OpenAlex

This chapter outlines the contributing factors that are changing the nature of manufacturing and their impact on the automotive industry. It provides a general description of the essential features of current manufacturing and automotive-related policies in the United States, Mexico, the EU, Germany and Spain. The chapter discusses the prominent role of state policies in the ongoing shifts in the automotive industry towards a new technological paradigm. In recent decades, the United States placed less emphasis on the relevance of manufacturing in comparison to its German counterparts. Moreover, the commercialization stage of innovation was largely seen as the purview of the private sector. The PIN 2020 seeks to position the Spanish automotive industry as one of the top automotive industries in the world, and it plans to do this by investing for the future in the most competitive market segments such as hybrid, electric and reduced emission vehicles.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.090
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.091
GPT teacher head0.293
Teacher spread0.202 · 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

Quick stats

Citations8
Published2015
Admission routes1
Has abstractyes

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