COACH-PLAYER COMMUNICATIONS: AN ANALYSIS OF TOP-LEVEL COACHING DISCOURSE AT A SHORT-TERM ICE HOCKEY CAMP
Bibliographic record
Abstract
Purpose: This study sought to analyze the instructional discourse of top-level coaches to identify the specific language content of coaching discourse in practice. Methodology: The study analyzed the recorded discourse of four coaches of the West Coast Hockey Prep Camp in Port Alberni, BC, Canada, between 2012 and 2016. Transcriptions of on-ice instructions were analyzed using Provalis QDA Miner v5.0.1 and Provalis WordStat v7.1.6 software to determine word-type and frequency. Main findings: The processed corpus of 21,376 words produced 1,022 quantifiable words which were classified into one or more of the categories of single-category language (i.e. General (G), General Slang (GSl), Sports Specific (SS), and Sports General (SG)), or the eight additional multi-category sub-categories (i.e. G/GSl, G/SS, G/SG, SS/SG, GSl/SG, G/SS/SG, G/GSl/SG, and GSl/SS/SG). Analyses revealed that single-category vocabulary (i.e. G, GSl, SS, and SG) made up 75.2% of the categorized language, with SS (4.6%) and SG (11.1%) making up 15.7% of the total. Applications: An understanding of the linguistic framework of instructional language in short-term training camps allows athletes to invest greater focus in their athletic performance in camp. The results offer athletes contextual reference for preparatory language study and authentic linguistic insight for the counter of potential target language anxiety. Novelty/Originality: Results indicate that top-level coaches relied significantly less on sports-specific word-type to facilitate their instruction and suggest that a general comprehension of English can provide a strong foundation for understanding top-level coaching discourse. This provides significant insight for athletes harboring concerns for English proficiency and coach-player miscommunication.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".