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Record W4392847741 · doi:10.1108/jole-01-2024-0006

Gender identity, inter-team competition and leader self-efficacy developmental trajectories in a multi-institutional leader development program

2024· article· en· W4392847741 on OpenAlexaff
David M. Rosch, Lisa Kuron, Robert D. Reimer, Ronald David Mickler, Daniel Jenkins

Bibliographic record

VenueJournal of Leadership Education · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicGender Diversity and Inequality
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsOriginalityCompetition (biology)Self-efficacyPsychologyValue (mathematics)Identity (music)Leadership developmentLeader developmentLongitudinal studyLongitudinal dataSocial psychologyDevelopmental psychologyPolitical sciencePublic relationsMedicineSociologyDemographyComputer science

Abstract

fetched live from OpenAlex

Purpose This study analyzed three years of data from the Collegiate Leadership Competition to investigate potential differences in longitudinal leader self-efficacy growth between students who identify as men and those who identify as women. Design/methodology/approach Survey design. Findings Results indicate that women participants enter their competition experience at higher levels of leader self-efficacy than men and that both groups were able to sustain moderate levels of growth measured several months after the end of the competition. Originality/value The gap between men and women in their leader self-efficacy did not change over the several months of measurement. Implications for leadership educators are discussed.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.269
GPT teacher head0.384
Teacher spread0.116 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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