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Record W4293217435 · doi:10.1177/10564926221119395

Authoritarianism, Populism, and the Global Retreat of Democracy: A Curated Discussion

2022· article· en· W4293217435 on OpenAlexaff
Paul S. Adler, Amr Adly, Daniel Erian Armanios, Julie Battilana, Zlatko Bodrožić, Stewart Clegg, Gerald F. Davis, Claudine Madras Gartenberg, Mary Ann Glynn, Ali Aslan Gümüşay, Heather A. Haveman, Paul M. Leonardi, Michael Lounsbury, Anita M. McGahan, Renate E. Meyer, Nelson Phillips, Kara Sheppard-Jones

Bibliographic record

VenueJournal of Management Inquiry · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Organizational Studies
Canadian institutionsUniversity of TorontoUniversity of Alberta
Fundersnot available
KeywordsAuthoritarianismDemocracyPopulismPolitical economySurpriseScholarshipLiberal democracyPopularityPolitical scienceGovernment (linguistics)SociologyDevelopment economicsLawPoliticsEconomics

Abstract

fetched live from OpenAlex

To the surprise of many in the West, the fall of the USSR in 1991 did not lead to the adoption of liberal democratic government around the world and the much anticipated “end of history.” In fact, authoritarianism has made a comeback, and liberal democracy has been on the retreat for at least the last 15 years culminating in the unthinkable: the invasion of a democratic European country by an authoritarian regime. But why does authoritarianism continue to spread, not only as an alternative to liberal democracy, but also within many liberal democracies where authoritarian leaders continue to gain strength and popularity? In this curated piece, contributors discuss some of the potential contributions of management scholarship to understanding authoritarianism, as well as highlight a number of directions for management research in this area.

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)0.006
metaresearch head score (Gemma)0.007
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: Commentary · Consensus signal: Commentary
Teacher disagreement score0.013
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0070.041
Scholarly communication0.0130.014
Open science0.0020.006
Research integrity0.0080.009
Insufficient payload (model declined to judge)0.0040.000

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 head0.018
GPT teacher head0.241
Teacher spread0.223 · 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
GenreCommentary

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

Citations67
Published2022
Admission routes1
Has abstractyes

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