Situation Awareness: A Pivotal Process for Sensemaking and Decision Making in the Learning and Practice of Physical Activities
Bibliographic record
Abstract
In physical education (PE), reflection on action is usually referred to in relation with pedagogical approaches such as experiential learning, constructivism and social constructivism. In organization systems, sensemaking has been discussed in relation with situation awareness (SA), a construct closely related not only to decision making but to understanding as well. In recent years, researchers interested in decision making in high-level sport performance have taken an interest in SA. The purpose of this explanatory article is to examine the applicability of the SA construct, including its related DM and sensemaking processes, to the teaching/learning and performing of diverse categories of physical activities such as sports, dance, fitness activities, outdoor activities and leisure activities in general. In a first section, the author distinguishes two types of SA, current SA and reflected SA, in relation with reflection in action and reflection on action. With regard to the involvement of one or several individuals, three SA facets are suggested: primary SA, distributed SA, and socially shared SA. Following a short discussion on the relationship between SA and the data/frame theory, the author examines the process of framing physical activities in view of situation awareness. Finally, the metacognitive side of framing and situation awareness is briefly discussed in terms of individuals who come to select particular observational cues that work better for them. Keywords: situation awareness, data/frame theory, sensemaking, decision making, frame building
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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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.023 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".