Reviewing Original Research Articles Published in the International Sport Coaching Journal
Bibliographic record
Abstract
The purpose of this commentary is to provide a broad overview of the empirical research-based articles published in the International Sport Coaching Journal from its inception in 2014 through 2020. Data from 101 publications were collected and analyzed using Arksey and O’Malley’s six-stage framework for conducting scoping reviews. Data were extracted on the size and scope of research, populations and perspectives studied, and methodologies and data collection methods used. The results show that empirical research publications grew more prominent over time (i.e., 24.0% of 2014 publications vs. 58.1% of 2020 publications) compared with other publication types. The most commonly researched topics included coach development and coach behaviors. The participants most studied were male coaches, performance sport coaches, and adult sport coaches, featuring primarily European and North American coaches. The majority of studies used a qualitative methodology with the most common research designs being phenomenological and case studies. A variety of data collection methods were used that involved one-on-one interviews and questionnaires. Several recommendations are advanced to stakeholders, including strategies to promote racial and gender diversity and to collect and report demographic data on race and coaching experience.
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 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 | 0.032 | 0.179 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.040 | 0.035 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".