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Record W2790666134 · doi:10.1080/10413200.2018.1449143

Setting the Conditions for Success: A Case Study Involving the Selection Process for the Canadian Forces Snowbird Demonstration Team

2018· article· en· W2790666134 on OpenAlexaffabout
Luc J. Martin, Mark Eys

Bibliographic record

VenueJournal of Applied Sport Psychology · 2018
Typearticle
Languageen
FieldPsychology
TopicTeam Dynamics and Performance
Canadian institutionsWilfrid Laurier UniversityQueen's University
Fundersnot available
KeywordsSelection (genetic algorithm)Process (computing)PsychologyApplied psychologyProcess managementMathematics educationManagement scienceArtificial intelligenceComputer scienceEngineering

Abstract

fetched live from OpenAlex

This case study investigated the selection process of a high-performance military team and explored potential implications for sport through an organizational psychology perspective. An instrumental case study was undertaken, comprising an observational visit and semistructured interviews with candidate (n = 3) and veteran (n = 2) pilots. Thematic analysis uncovered a range of strategies utilized for the selection of ideal candidates (e.g., flight briefings, systematic flight progressions, mentorship, traditional events), and these are described in relation to candidate and veteran perceptions and are contextualized with regards to candidate motivation for membership and the broader team environment. A number of identified concepts have relevance to sport and are discussed in relation to both theory and practice.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.248
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.030
GPT teacher head0.382
Teacher spread0.352 · 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 teacher head, not a consensus.

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

Citations6
Published2018
Admission routes2
Has abstractyes

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