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Record W6908355784 · doi:10.26181/21859638

Visualising Parkour

2023· article· en· W6908355784 on OpenAlexaboutno aff

Bibliographic record

VenueLa Trobe University · 2023
Typearticle
Languageen
FieldPsychology
TopicAdventure Sports and Sensation Seeking
Canadian institutionsnot available
Fundersnot available
KeywordsEthnographyComicsField (mathematics)Participant observationThe InternetWork (physics)

Abstract

fetched live from OpenAlex

Submission note: Submitted in total fulfilment of the requirements of the degree of Doctor of Philosophy (by research and dissertation in the discipline of Anthropology) for the School of Humanities and Social Sciences College of Art, Social Sciences and Commerce, La Trobe University, Victoria.<br><br>This thesis is a visual ethnography of the international parkour subculture. It is based on five years of multi-fielded ethnographic research. Participant observation, interviews, Internet correspondence and secondary materials are drawn on to describe parkour communities across eighteen international fields. Data is drawn from a number of field sites across Australia, America, Canada, Denmark, England, France, The Ukraine and Russia. Utilizing ethnographic description and the work of Bourdieu, as well as a number of interdisciplinary sources, this thesis provides an overview of the similarities and differences in parkour communities and ideologies. It offers a model of common parkour community dynamics and looks at the local, national and international forces that contribute to differences in parkour communities and practice in specific localities. Visual materials like photos and illustrated comics are utilised to deliver ethnographic materials. This thesis contributes to understandings of the development of new international movement subcultures and lifestyle sports, as well as the interaction of local and global forces in the ways that these subcultural practices manifest and develop.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.735
Threshold uncertainty score0.913

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.025
GPT teacher head0.294
Teacher spread0.269 · 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 designNot applicable
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

Citations0
Published2023
Admission routes1
Has abstractyes

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