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Record W3158629838 · doi:10.1123/smej.2020-0040

Undergraduate Sport Management Education: Exploring Ego Development and Leadership Efficacy

2021· article· en· W3158629838 on OpenAlexafffund
Shannon Kerwin, Kirsty Spence

Bibliographic record

VenueSport Management Education Journal · 2021
Typearticle
Languageen
FieldPsychology
TopicEgo Development and Educational Practices
Canadian institutionsBrock University
FundersBrock University
KeywordsId, ego and super-egoPsychologyLoevinger's stages of ego developmentConstruct (python library)Leadership developmentPerceptionExperiential learningSelf-efficacyRelation (database)Transactional leadershipSocial psychologyPedagogyPublic relationsComputer sciencePolitical science

Abstract

fetched live from OpenAlex

This research explored students’ ego development and leadership efficacy during an undergraduate sport management program. A sequential mixed-method case study design and associated methodologies were adopted to explore students’ ego development and leadership efficacy during 3 years of an academic program. Results show fluctuations in leadership efficacy for all but one participant. These fluctuations are discussed in relation to ego development in that growth from self-conforming to self-authoring stages of ego development may partially explain fluctuations in how students see themselves and their potential for leadership in the field of sport management. The role of the ego development construct in relation to students’ perceptions of their leadership capabilities highlights that programmatic elements (e.g., thoughtful experiential education) can be consciously developed and strategically leveraged to more accurately target perceptions of leadership prowess among students. The findings emphasize that students’ level of ego development can be fostered through active and effective program delivery.

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)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.160
GPT teacher head0.349
Teacher spread0.189 · 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

Citations5
Published2021
Admission routes2
Has abstractyes

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