Research-informed and Evidence-based Quality Assurance and Enhancement in Amateur/Grassroots Football: Strategic Educational Inquiry for Coach Leaders/Administrators
Bibliographic record
Abstract
Coach leaders/administrators in diverse amateur and grassroots football contexts are increasingly accountable for sustaining strategic, state-of-the-art, evidence-based, effective, and efficient programs, initiatives, and services. However, coach leaders/administrators within these organizational settings face significant challenges (e.g., insufficient organizational support and research expertise) in enacting research-informed and evidence-based practices. Strategic Educational Inquiry (SEI) is a flexible and rigorous approach to practitioner research and is particularly useful for coach leaders/administrators to gather evidence for quality assurance and enhancement purposes. This paper critically examines whether and how SEI is applied in diverse amateur/grassroots football coaching contexts. Drawing on case study research using multiple case design, preliminary findings from this study indicate that SEI situates specific amateur/grassroots coaching programs and initiatives within the relevant research and professional literature; it focuses SEI on organization-specific priority research objectives, ethical inquiry, and appropriately aligned research methodology; and involves systematic data collection, data analysis, and dissemination of best practices. Critical organization-specific supports to facilitate implementation of SEI in diverse amateur/grassroots football contexts include: strategic coach education and skills training (e.g., access to state-of-the-art customized technology-enabled professional development experiences and expert mentoring support).
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 | 0.237 | 0.245 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.019 | 0.009 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".