Bibliographic record
Abstract
<JATS1:p>This book is a collection of essays written by teacher educators with a passion for sharing knowledge, research, and insights about the Teaching Games for Understanding (TGfU) approach. The intended audience for this collection is teacher educators and graduate and undergraduate students in physical education and sport pedagogy as well as practicing teachers and coaches interested in helping students and players to become better games players.</JATS1:p> <JATS1:p>All our authors presented at the Fourth International TGfU Conference, which provided an arena for a restatement of the importance of games as a learning process. The conference was held in May 2008 at the University of British Columbia (UBC) in Vancouver, Canada. The next seminar will begin the newly adopted four-year cycle in 2012 in the United Kingdom and will be hosted by Loughborough University in Leicestershire. This event will mark the 30th anniversary of the seminal TGfU paper written by Bunker and Thorpe in 1982.</JATS1:p> <JATS1:p>TGfU has become a significant movement in physical education worldwide. The 2008 TGfU conference was attended by 355 participants (150 teachers, 40 coaches, 40 graduate students, and 125 researchers and teacher educators) who represented 26 countries from 6 continents. Over 90 presentations, including 22 practical sessions, took place at the conference. The 2008 conference was able to achieve a more desirable balance in research, theory, and practice. The planning committee spent a great deal of energy targeting the K-12 schools, coaching organizations, and their networks.</JATS1:p>
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.023 | 0.007 |
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".