The applied model for the use of observation: An update on context and function research
Bibliographic record
Abstract
A review of the literature on the use of observation for motor learning and performance was conducted by Ste-Marie et al. (2012). Of the close to 100 articles reviewed in that work, it was determined that close to 95% were undertaken in skill training contexts with little research in sport competition or rehabilitation contexts. Moreover, the majority of the skill training research (>80%) was completed in laboratory settings. Additionally, the observation interventions used targeted the skill function of observation (> 90%) and interventions related uniquely to the strategy and performance functions were largely ignored. Ste-Marie et al. (2012) recommended that researchers extend their inquiry on the use of observation interventions beyond the skill function and to implement the research in applied settings that also captures the sport competition and rehabilitation contexts. An analysis of the observation intervention research literature from 2012-2018 was conducted to determine whether any of these recommendations have been heeded. The analysis of the 57 articles garnered has shown a greater percentage of research in the sport competition and rehabilitation contexts (30%), yet research in training contexts was still dominant. Of those papers in the training context, close to 50% were completed in applied settings. Consistent with the early research, however, observation interventions were focused on the skill function, with limited interventions for the performance or strategy functions. Thus, while some recommendations have been heeded, there are still research gaps that need to be addressed within observation intervention research for motor skill learning 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 | 0.037 | 0.089 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".