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Record W2053504042 · doi:10.1080/09537325.2012.663962

The future of automobile society: a socio-technical transitions perspective

2012· article· en· W2053504042 on OpenAlexaboutno aff
Maurie J. Cohen

Bibliographic record

VenueTechnology Analysis and Strategic Management · 2012
Typearticle
Languageen
FieldEngineering
TopicSustainable Industrial Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsPerspective (graphical)Sociotechnical systemEngineering ethicsSociologyEngineeringRegional scienceManagement scienceComputer scienceKnowledge managementArtificial intelligence

Abstract

fetched live from OpenAlex

Automobile society has been triumphant for a century. While this success is often ascribed to entrepreneurial tenacity and indefatigable demand, it is more correctly credited to auspicious political, economic and cultural trends. The macro-scale factors responsible for the entrenchment of automobility in developed countries are now moving in reverse direction. A socio-technical transitions perspective emphasises how declining industrial influence, stagnating wages, growing income inequality, increasing vehicle operating costs and changing sociodemographics are now undermining the foundations of automobile society. Three expressions of this process are considered: claims that transport planners are engaged in a ‘war’ against the automobile, emergent evidence that vehicle use is reaching saturation (the so-called ‘peak car’ phenomenon) and apparent disinclination of youth to embrace automobile-oriented lifestyles. Although these developments suggest some instability in the socio-technical system, the lock-in of key features and the paucity of practicable alternatives suggest that declarations of a pending transition are premature.

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.

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)0.002
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.995
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0050.013
Scholarly communication0.0070.008
Open science0.0010.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0060.000

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 head0.007
GPT teacher head0.234
Teacher spread0.226 · 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.

Study designTheoretical or conceptual
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

Citations107
Published2012
Admission routes1
Has abstractyes

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