MétaCan
Menu
Back to cohort
Record W4283031244 · doi:10.3389/fspor.2022.926974

Talent Identification and Development in Paralympic Contexts: Current Challenges

2022· review· en· W4283031244 on OpenAlex
Nima Dehghansai, Ross A. Pinder, Joe Baker

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueFrontiers in Sports and Active Living · 2022
Typereview
Languageen
FieldMedicine
TopicCardiovascular Effects of Exercise
Canadian institutionsYork University
Fundersnot available
KeywordsIdentification (biology)AthletesTalent developmentPsychologyMedicinePhysical therapyPedagogy

Abstract

fetched live from OpenAlex

This short review explores the state of talent identification and development of athletes in Paralympic contexts. While talent identification typically occurs during adolescence, this practice is more complex and variable in Paralympic contexts compared to non-Paralympic contexts. For example, Paralympic athletes can have impairments that are congenital or acquired at any time across their lives. Therefore, they can enter performance pathways at unpredictable times. Furthermore, differences and nuances associated with athlete impairments (type and severity), compounded by other systematic complexities (e.g., systems of classification) highlight the need to consider alternative and creative approaches to talent identification and development. We provide an overview of some of these complexities, highlight areas for future research, and provide recommendations for practitioners.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.987
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
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 head0.034
GPT teacher head0.303
Teacher spread0.270 · 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