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Record W2883871796 · doi:10.1177/1742715018787909

Leadership effectiveness and the problem of social action: Continuing the conversation between Burns and Rost

2018· article· en· W2883871796 on OpenAlexaff
Stan Amaladas

Bibliographic record

VenueLeadership · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Organizational Studies
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsConversationAction (physics)SociologyPoliticsLeadership studiesEducational leadershipLeadershipField (mathematics)EpistemologyTransactional leadershipEnvironmental ethicsLeadership stylePolitical sciencePedagogyPublic relationsLawPhilosophy

Abstract

fetched live from OpenAlex

While calling for the need to continue engaging in conversation with differing understandings of leadership effectiveness, the author demonstrates what this could look like in practice by conversing with three leadership authors, Hannah Arendt, from the field of Political Theory, James McGregor Burns from the disciplines of Political Science and American History, and Joseph C Rost, from the field of Education Administration/Leadership. In this article, the author seeks to understand differing and contradictory notions of leadership effectiveness as they are proposed by Burns and Rost. Following Arendt, the author (a) distinguishes the nature of action as understood by Burns (acting as making) and Rost (acting as initiating), (b) outlines the dominant influence of ‘acting as making’ or homo-faber image of leadership within the discipline of leadership studies, and (c) considers the implications of including ‘acting as initiating’ for leadership learning and education.

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)0.036
metaresearch head score (Gemma)0.039
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.036
Threshold uncertainty score0.189

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0360.039
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0120.073
Scholarly communication0.0150.037
Open science0.0030.007
Research integrity0.0120.022
Insufficient payload (model declined to judge)0.0020.000

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 head0.083
GPT teacher head0.252
Teacher spread0.169 · 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 designTheoretical or conceptual
Domainnot available
GenreOther

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

Citations1
Published2018
Admission routes1
Has abstractyes

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