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Record W2737153543 · doi:10.1108/jmh-04-2017-0018

From the industrial revolution to Trump

2017· article· en· W2737153543 on OpenAlexaff
Anthony M. Gould, Michael Bourk, Jean‐Etienne Joullié

Bibliographic record

VenueJournal of Management History · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Organizational Studies
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsLegitimacyPresidential systemPoliticsOriginalityArgument (complex analysis)Presidential electionSociologyValue (mathematics)Business historyPublic sectorState (computer science)Object (grammar)Political economyPublic administrationPublic relationsPolitical scienceEconomicsLawSocial scienceManagement

Abstract

fetched live from OpenAlex

Purpose This paper takes a long-term view of how the US public and private sectors have been viewed in relation to each other. It notes that since the time of approximately the Nixon Administration, each sector has not been viewed favourably by the public. Over the past 40 years, the private sector has been perceived as being run by the unscrupulous and the public sector by incompetents. The essay argues that Donald Trump was able to exploit these circumstances to win the 2016 election. Design/methodology/approach This paper presents a polemic. It relies on archival research and data to create a new view of historical eras in US business history. The object of analysis is the idea of relative legitimacy, the public image of the State vis-a-vis business and business managers. Findings Although the paper addresses business history, a novel argument is presented about the 2016 US Presidential election. It is proposed that Trump took advantage of unique historical circumstances; therefore, his win had more to do with the moment than with him personally. Research limitations/implications The paper interprets the 2016 Presidential race as the end-point of a 250-year journey. It sets a new agenda, in that previous analyses have mostly viewed the ascendancy of Trump as pertaining to distinctively post-industrial twenty-first-century phenomena. Social implications In analysing the 2016 Presidential race, the emphasis is largely removed from issues of personality or partisan politics. Originality/value The paper takes a view of the 2016 election which has not hitherto been adopted. It proposes a new concept – relative legitimacy – as having a substantial explanatory value.

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)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.018
Scholarly communication0.0070.006
Open science0.0010.004
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0090.001

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 head0.045
GPT teacher head0.218
Teacher spread0.173 · 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 designNot applicable
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

Citations13
Published2017
Admission routes1
Has abstractyes

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