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Record W3096365924

Character in leadership: Perceptions from intercollegiate athletics administrators

2020· article· en· W3096365924 on OpenAlexaffabout
Karen Danylchuk, Zachary Weese, Kyle F. Paradis

Bibliographic record

VenueUlster University Research Portal (Ulster University) · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicLegal Issues in Education
Canadian institutionsWestern University
Fundersnot available
KeywordsPsychologyCollege athleticsCharacter (mathematics)PerceptionSocial psychologyApplied psychologyPolitical scienceHigher education
DOInot available

Abstract

fetched live from OpenAlex

Despite the importance of leadership in sport organizations and the extensive research conducted in leadership, the construct of leader character has received less research attention. This study examined the prevalence, perceived importance, and perceived value of leader character within Canadian intercollegiate athletics administration. A total of 116 Athletic Administrators at every Canadian U-Sports member institution were contacted to participate in the study. Seventy-six administrators agreed to participate yielding a response rate of 65.5%. Leader character was measured using the Leader Character Insight Assessment (LCIA) consisting of 11 leader character dimensions. Overall, Accountability, Integrity, and Drive were the highest ranked leader character dimensions while the lowest ranked leader character dimensions included Humility, Justice, Temperance, and Transcendence. The participants also perceived that their universities valued leader character similarly to themselves. The understanding of leader character within Canadian intercollegiate athletics administration is advanced and the importance of leader character is 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)0.003
metaresearch head score (Gemma)0.009
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 score0.187
Threshold uncertainty score0.372

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.002
Scholarly communication0.0020.001
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 head0.209
GPT teacher head0.362
Teacher spread0.154 · 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

Citations1
Published2020
Admission routes2
Has abstractyes

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