Movements Used in Storytelling: Indigenous Perspectives for Motor Learning
Bibliographic record
Abstract
Within motor learning, there is limited literature from the perspective of Indigenous ways of knowing and doing. The purpose of this ongoing research journey is to explore the role of movement within the oral traditions of Indigenous storytelling. In the present study, five Elders and Knowledge Keepers from communities in the Lower Mainland of British Columbia participated in three sharing circles. Discussion focused on the relationship between learning movement-related skills and storytelling. The data was then analyzed using a modified approach to Braun and Clarke’s (2006) thematic analysis methodology. The findings revealed teachings and ideas focused on the ways the storyteller moves to tell a story. Strategies focused on fine and gross motor skills, including the use of facial expressions, hand movements, and body movements. Participants identified that face and hand movements are used to facilitate guided discovery in story-listeners (e.g., to describe actions in stories that are not demonstrated). Participants also explained that storytellers and story-listeners use body movements to promote retention and intrinsic motivation (e.g., to create a motivating and engaging environment). A strength-based approach was also emphasized when using movements as a learning strategy. Indigenous knowledge systems are built upon relationships; thus, the reflexivity between movement used to tell stories and story-listeners providing feedback on the effectiveness of the storyteller’s movements may be a central aspect of motor learning from an Indigenous lens. Story is foundational to most Indigenous traditions and acknowledging its role within motor learning pedagogy is an important area for growth in the field.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.025 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".