Observation interventions for motor skill learning and performance: an applied model for the use of observation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Using the 5 Ws and 1 H journalistic approach of Beveridge Mackie (2011), we reviewed the observation intervention research that targeted sport skills or daily movement tasks. Through this review, it became apparent that while there is much research that examines observation of a live or video (what), skilled model (who) for enhanced skill learning (why) in laboratory settings (where), there is a need for not only a wider scope of research, but also a deeper one. Following the review of literature, an applied model for the use of observation is advanced. Through this applied model, we propose that practitioners should first assess the observer's characteristics and the task characteristics for which any observation intervention is being created. The practitioner should then gain an understanding of the context and the desired outcomes of the learner and use this advance information to vary the characteristics of: (1) who is observed; (2) what is observed and what instructional features will accompany the intervention; (3) when it is observed; and (4) how the observed information should be delivered. Future research directions are also forwarded with regard to identified gaps in the literature.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it