The Coach development framework for the International Ice Hockey Federation
Bibliographic record
Abstract
The here presented Master’s Thesis had as its objective to create a Coach Development Framework (CDF) for the International Ice Hockey Federation (IIHF). \n \nThe main phases of the thesis work included benchmarking of well-developed and extensive coach education frameworks from countries that have a well-established sport governance such as the USA, Canada, United Kingdom, New Zealand, Ireland, Australia, and South Africa, as well as the International Sport Coaching Framework and the European Sport Coaching Framework by the International Council For Coaching Excellence. The literature review included besides the different sport frameworks also peer-reviewed articles for certain chapters of the IIHF CDF as well as books by renowned experts on coaching topics. The benchmarking of this coach development literature was the foundation for the creation of the thesis product, the International Ice Hockey Federation Coach Development Framework (IIHF CDF). The IIHF CDF was reviewed by a number of experts from different countries whose comments and suggestions were gladly incorporated in the final stage of the work before submission. The IIHF CDF is presented in its entirety in the appendix to this thesis. \n \nThe result of the thesis work is the actual IIHF CDF (see appendix). Introduction, background, work progress, literature review, Coach Development System/Programme recommendations, discussion and conclusions are presented in the present document. \n \nConcluding it can be said that athlete development is only possible when the coaches working with the athletes possess the necessary core competences and capabilities, understand the premise of athlete-centred coaching and internalize life-long learning.
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 | 0.018 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| 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".