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Record W2464778239 · doi:10.17645/pag.v4i2.582

Leadership in Precarious Contexts: Studying Political Leaders after the Global Financial Crisis

2016· article· en· W2464778239 on OpenAlexaff
Cristine de Clercy, Peter Ferguson

Bibliographic record

VenuePolitics and Governance · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicPolitical and Economic history of UK and US
Canadian institutionsWestern University
Fundersnot available
KeywordsScholarshipPoliticsPolitical scienceFinancial crisisContext (archaeology)Perspective (graphical)Face (sociological concept)Political economyCrisis managementGlobal LeadershipLeadership studiesPublic relationsLeadership styleSociologySocial scienceEconomicsLawHistory

Abstract

fetched live from OpenAlex

A series of crises and traumatic events, such as the 9/11 attacks and the 2008 global financial crisis, seem to have influenced the environment within which modern political leaders act. We explore the scholarly literature on political leadership and crisis since 2008 to evaluate what sorts of questions are being engaged, and identify some new lines of inquiry. We find several scholars are contributing much insight from the perspective of leadership and crisis management. Several analysts are investigating the politics of crisis from a decentralist perspective, focusing on local leadership in response to challenging events. As well, studying how citizens interpret, respond to, or resist leaders’ signals is a developing area of inquiry. While our study reveals some debate about the nature of crisis, and whether the context has changed significantly, most of the scholarship reviewed here holds modern politicians face large challenges in exercising leadership within precarious contexts.

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)0.004
metaresearch head score (Gemma)0.008
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.007
Scholarly communication0.0060.004
Open science0.0010.003
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0030.001

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 head0.072
GPT teacher head0.285
Teacher spread0.212 · 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

Citations22
Published2016
Admission routes1
Has abstractyes

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