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Research Note: Are executive leaders in sport as good as they think?

2000· article· en· W4400901747 on OpenAlexaff
W. James Weese

Bibliographic record

VenueEuropean journal for sport management. · 2000
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicLeadership and Management in Organizations
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsPsychologyPolitical sciencePublic relationsManagementEconomics

Abstract

fetched live from OpenAlex

Leadership continues to be a popular research topic in a number of fields including sport management. Various valid and reliable instruments exist to quantitatively measure executive leaders, typically from the perspectives of either the leaders themselves (SELF form) or from the perspectives of superiors and/or subordinates (OTHER form), or both. However, some theorists (Ashford, 1989; Atwater and Yammarino, 1992) have questioned the utility of self-reports, noting that leaders’ assessments of themselves are generally inflated, inaccurate, unreliable, and unrelated to organizational outcomes.The author examined five data sets for 192 sport leaders and their staff members to determine if they also significantly inflated their perceptions of their leadership. In each of the data sets, the leaders’ perceptions of their leadership tendencies were found statistically to be higher than the opinions of their staff. These results offer many implications for sport management researchers contemplating using “self’ reports exclusively to measure executive leadership.

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)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0220.004

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 head0.048
GPT teacher head0.305
Teacher spread0.257 · 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 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

Citations3
Published2000
Admission routes1
Has abstractyes

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