Alignment of qualitative and quantitative focus of attention assessment of stone delivery among high-level Canadian curlers
Bibliographic record
Abstract
To understand the alignment of current focus strategies of high-level Canadian curlers and to investigate if the focus of attention theory differentially affects draw and takeout deliveries, a mixed-methods approach was used. Eleven high-level Canadian curlers (N = 11) delivered draws and takeouts of both handles (in-turn/out-turn) under control, internal focus, and external focus conditions. To prevent knowledge of performance and results, the curlers vision and hearing were occluded post-delivery. Pre-experiment questions pertained to the focus strategy. Post-experiment questions pertained to focus preference. Open-ended questions were analysed via thematic analysis. Draw radial error, absolute constant error, and variable error scores were analysed via separate repeated measures ANOVA with Tukey’s Honestly Significant Difference post-hoc test. Takeout end-point accuracy (hit/miss) were analysed by handle via a one-way Cochrane Q test with McNemars test as a post-hoc. Curler’s self-regulated focus strategy used a multifaceted focus approach with multiple internal and external foci. For draws, regardless of handle, an internal focus increased radial error compared to control. In contrast with actual performance, athletes favoured an internal focus due to the perceived importance placed on “touch”. Conversely, athletes preferred an external focus for out-turn takeouts which increased hitting rate over an internal focus, and was favoured for the increased sensation of “line maintenance” which aligned with performance. Draw and takeout end-point accuracy were differentially affected by focus. A multifaceted attentional approach should be considered to align perception and performance.
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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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".