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Record W7033950746

The Social Identity Approach to Leadership: The Case of Alberta

2021· article· en· W7033950746 on OpenAlexaffabout

Bibliographic record

VenueScholarship@Western (Western University) · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicStudy of Mite Species
Canadian institutionsWestern University
Fundersnot available
KeywordsSocial identity theoryPoliticsIdentity (music)Collective identityLeadership studiesShared leadershipCollective leadershipSocial group
DOInot available

Abstract

fetched live from OpenAlex

The study of political leadership within the discipline of the political science has recently grown into a large, complex, and insightful literature. However, the extensive number of concepts, theories, and frameworks developed by international leadership scholars have been underutilized when it comes to developing further understanding of political leadership in the Canadian context. This thesis attempts to address this gap by focusing on the process by which individuals are selected to be leaders. I utilizes social psychology and Identity Leadership Theory to theorize that leaders are successful to the extent that are able to cohere with broader group processes by articulating group characteristics, establishing individual prototypicality, and entrenching their policy agenda in pre-existing collective identities and understandings. The study develops and examines a concise causal relationship and hypotheses through a case study of the Albertan provincial context. This comprises analyses of two premiers, William Aberhart and Peter Lougheed, that utilize a set of primary communicative sources to examine the substantive components of their successful leadership appeal. Overall, this thesis’s findings suggest that these leaders were successful despite not meeting the expectations of the analysis’s hypotheses. Consequently, it is concluded that Identity Leadership Theory is not an accurate or useful means by which to understand political leadership in Canada.

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 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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.115
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.187
GPT teacher head0.310
Teacher spread0.122 · 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 teacher head, not a consensus.

Study designObservational
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

Citations0
Published2021
Admission routes2
Has abstractyes

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