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Record W2030035443 · doi:10.1080/17408989.2014.895802

The ‘function-to-flow’ model: an interdisciplinary approach to assessing movement within and beyond the context of climbing

2014· article· en· W2030035443 on OpenAlexaffabout
Rebecca Lloyd

Bibliographic record

VenuePhysical Education and Sport Pedagogy · 2014
Typearticle
Languageen
FieldPsychology
TopicSport Psychology and Performance
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsCurriculumContext (archaeology)Class (philosophy)Data collectionClimbingPsychologyMathematics educationPhysical educationMedical educationPedagogyMedicineSociologyComputer scienceGeography

Abstract

fetched live from OpenAlex

Background: Physical Education (PE) programmes are expanding to include alternative activities yet what is missing is a conceptual model that facilitates how the learning process may be understood and assessed beyond the dominant sport–technique paradigm.Purpose: The purpose of this article was to feature the emergence of a Function-to-Flow (F2F) interdisciplinary curriculum support tool which facilitates the assessment of an alternative activity beyond baseline levels of participation.Participants and setting: Participants of this study include a sample of N = 153 students from grades one, five, seven, eight, and nine located within seven different schools in Ottawa (Canada), who booked the JungleSport™ climbing programme of their own accord. Within each school, a particular class was selected under the guidance of the school principal and in consultation with the Health and PE teacher. The Head Instructor of the JungleSport™ programme also participated in the study. During the second year of this study, three classes from three different intermediate schools, including two grade 7 classes (n = 19 and n = 26) and a grade 8 class (n = 23), piloted curriculum support tools that are featured in this article.Data collection: The main source of information upon which this article is based is the small group interviews with student participants that were conducted at the conclusion of the three- to five-day JungleSport™ programme. Secondary sources of information include journal entries from prepared booklets, student, teacher, and head instructor interviews, as well as observed interdisciplinary activities.Data analysis: The main question that guided this three-year study was: What is it like to become physically educated in a way that invites an expanded movement consciousness, from the rudiments of movement function to the somatics of flow? To explore this question, several student-friendly sub-questions were developed. Responses to these questions were compiled into a summative chart, the interdisciplinary F2F curriculum support tool, which depicts how a student may climb at different levels of proficiency.Findings: Assessing movement in relation to the F2F model has the potential to facilitate an appreciation and understanding of the many ways a student may become physically educated. This article features how students were able to articulate understandings of muscular function, desired form, kinaesthetic feeling, as well as existential possibilities for experiencing flow. It is postulated that a similar curriculum support tool could be designed for other activities, however alternative, as well as mainstream sport. Simply analysing movement experiences in terms of muscular function, form, feeling, and flow as exemplified in this study has the potential to broaden narrowed conceptions of learning in the PE context. Thus the ‘physical education-as-sport- technique’ paradigm which largely attends to isolated form has the potential to become the ‘function–form–feeling–flow’ paradigm. Thus, in closely observing students climbing, as described in the first year of this project and co-reflecting on the percepts and concepts that emerge in relation to the F2F-inspired curriculum support tool as featured in this article, assessment and interdisciplinary understanding in PE has the potential to reach new heights.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.684
Threshold uncertainty score0.313

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.030
GPT teacher head0.407
Teacher spread0.377 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2014
Admission routes2
Has abstractyes

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