The scope of intentionality supporting the design of played-form practice activities in soccer
Bibliographic record
Abstract
Played-form practice in team sports like soccer is being studied with increasing popularity for the effect of manipulations made to the original settings of the game. However, the efficiency of practice sessions in leading towards improvement can be subject to challenges if, despite thoughtful design, activities depend on the objectivity of their design. To achieve better outcomes, coaches are, therefore, expected to clarify their intentionality and use this information to address diagnosed issues efficiently. There exist no pre-established lists of intentions for coaches to pursue, although the literature about the game, its core, and its principles all offer significant solutions to align the configuration of activities to intended effects towards participants. In this paper, we suggest two series of intentions where tactics and player-coach relations are, respectively, at the centre of the considerations. Namely, “triggering, fine-tuning, awakening, situating, challenging, adapting or maintaining, and preparing” are suggested keywords communicating a clear intentionality based on tactics. For the sake of applicability, we also present examples of designs based on our suggestions and practical implications on played-form practice, right from its design. A key message is: intentionality reflects an important thinking element in practice design as it serves to maintain organised practice activities relevant and efficient.
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 | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".