Bibliographic record
Abstract
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. 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.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.061 | 0.006 |
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".